May 15, 2022 · 1. We propose an unsupervised method to distinguish in-distribution from out-of-distribution input. The results indicate that the assumptions and methods of outlier and deep anomaly detection are also relevant to the field of out-of-distribution detection. 2. The method works on the basis of an Isolation Forest. We evaluate our method on a diverse set of in- and out-of-distribution dataset pairs. In many settings, our method outperforms other methods by a large margin. The contri-butions of our paper are summarized as follows: • We propose a novel experimental setting and a novel training methodology for out-of-distribution detection in neural networks. Mar 2, 2020 · Out-of-Distribution Generalization via Risk Extrapolation (REx) Distributional shift is one of the major obstacles when transferring machine learning prediction systems from the lab to the real world. To tackle this problem, we assume that variation across training domains is representative of the variation we might encounter at test time, but ... Mar 21, 2022 · Most of the existing Out-Of-Distribution (OOD) detection algorithms depend on single input source: the feature, the logit, or the softmax probability. However, the immense diversity of the OOD examples makes such methods fragile. There are OOD samples that are easy to identify in the feature space while hard to distinguish in the logit space and vice versa. Motivated by this observation, we ... novelty detection (ND), open set recognition (OSR), out-of-distribution (OOD) detection, and outlier detection (OD). These sub-topics can be similar in the sense that they all define a certain in-distribution, with the common goal of detecting out-of-distribution samples under the open-world assumption. However, subtle differences exist among ... novelty detection (ND), open set recognition (OSR), out-of-distribution (OOD) detection, and outlier detection (OD). These sub-topics can be similar in the sense that they all define a certain in-distribution, with the common goal of detecting out-of-distribution samples under the open-world assumption. However, subtle differences exist among ... Oct 28, 2022 · Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from input data through a model. This problem has attracted increasing attention in the area of machine learning. OOD detection has achieved good intrusion detection, fraud detection, system health monitoring, sensor network event detection, and ecosystem interference detection. The method based on deep ... Aug 29, 2023 · ODIN is a preprocessing method for inputs that aims to increase the discriminability of the softmax outputs for In- and Out-of-Distribution data. Implements the Mahalanobis Method. Implements the Energy Score of Energy-based Out-of-distribution Detection. Uses entropy to detect OOD inputs. Implements the MaxLogit method. out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maxi-mum softmax probabilities than erroneously classified and out-of-distribution ex-amples, allowing for their detection. We assess performance by defining sev- Mar 3, 2021 · Then, we focus on a certain class of out of distribution problems, their assumptions, and introduce simple algorithms that follow from these assumptions that are able to provide more reliable generalization. A central topic in the thesis is the strong link between discovering the causal structure of the data, finding features that are reliable ... Mar 3, 2021 · Then, we focus on a certain class of out of distribution problems, their assumptions, and introduce simple algorithms that follow from these assumptions that are able to provide more reliable generalization. A central topic in the thesis is the strong link between discovering the causal structure of the data, finding features that are reliable ... Let Dout denote an out-of-distribution dataset of (xout;y out)pairs where yout 2Y := fK+1;:::;K+Og;Yout\Yin =;. Depending on how different Dout is from Din, we categorize the OOD detection tasks into near-OOD and far-OOD. We first study the scenario where the model is fine-tuned only on the training set D in train without any access to OOD ... Sep 3, 2023 · Abstract. We study the out-of-distribution generalization of active learning that adaptively selects samples for annotation in learning the decision boundary of classification. Our empirical study finds that increasingly annotating seen samples may hardly benefit the generalization. To address the problem, we propose Counterfactual Active ... cannot deliver reliable reasoning results when facing out-of-distribution samples. Next, even if supervision signals can be properly propagated between the neural and symbolic models, it is still possible that the NN predicts spurious fea-tures, leading to bad generalization performance (an exam-ple is provided in Sec. 6). Dec 25, 2020 · Out-of-Distribution Detection in Deep Neural Networks Outline:. A bit on OOD. The term “distribution” has slightly different meanings for Language and Vision tasks. Consider a dog... Approaches to Detect OOD instances:. One class of OOD detection techniques is based on thresholding over the ... We evaluate our method on a diverse set of in- and out-of-distribution dataset pairs. In many settings, our method outperforms other methods by a large margin. The contri-butions of our paper are summarized as follows: • We propose a novel experimental setting and a novel training methodology for out-of-distribution detection in neural networks. ODIN: Out-of-Distribution Detector for Neural Networks Mar 25, 2022 · All solutions mentioned above, such as regularization, multimodality, scaling, and invariant risk minimization, can improve distribution shift and out-of-distribution generalization, ultimately ... Aug 31, 2021 · This paper represents the first comprehensive, systematic review of OOD generalization, encompassing a spectrum of aspects from problem definition, methodological development, and evaluation procedures, to the implications and future directions of the field. Jun 21, 2021 · 1. Discriminators. A discriminator is a model that outputs a prediction based on sample’s features. Discriminators, such as standard feedforward neural networks or ensemble networks, can be ... Mar 25, 2022 · All solutions mentioned above, such as regularization, multimodality, scaling, and invariant risk minimization, can improve distribution shift and out-of-distribution generalization, ultimately ... Jan 22, 2019 · Out-of-distribution detection using an ensemble of self supervised leave-out classifiers A. Vyas, N. Jammalamadaka, X. Zhu, D. Das, B. Kaul, and T. L. Willke, “Out-of-distribution detection using an ensemble of self supervised leave-out classifiers,” in European Conference on Computer Vision, 2018, pp. 560–574. We have summarized the main branches of works for Out-of-Distribution(OOD) Generalization problem, which are classified according to the research focus, including unsupervised representation learning, supervised learning models and optimization methods. For more details, please refer to our survey on OOD generalization. novelty detection (ND), open set recognition (OSR), out-of-distribution (OOD) detection, and outlier detection (OD). These sub-topics can be similar in the sense that they all define a certain in-distribution, with the common goal of detecting out-of-distribution samples under the open-world assumption. However, subtle differences exist among ... [ICML2022] Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities [ICML2022] Scaling Out-of-Distribution Detection for Real-World Settings [ICML2022] POEM: Out-of-Distribution Detection with Posterior Sampling [NeurIPS2022] Deep Ensembles Work, But Are They Necessary? out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maxi-mum softmax probabilities than erroneously classified and out-of-distribution ex-amples, allowing for their detection. We assess performance by defining sev- The outputs of an ensemble of networks can be used to estimate the uncertainty of a classifier. At test time, the estimated uncertainty for out-of-distribution samples turns out to be higher than the one for in-distribution samples. 3. level 2. AnvaMiba. cause of model crash under distribution shifts, they propose to realize out-of-distribution generalization by decorrelat-ing the relevant and irrelevant features. Since there is no extra supervision for separating relevant features from ir-relevant features, a conservative solution is to decorrelate all features. Dec 17, 2019 · The likelihood is dominated by the “background” pixels, whereas the likelihood ratio focuses on the “semantic” pixels and is thus better for OOD detection. Our likelihood ratio method corrects the background effect and significantly improves the OOD detection of MNIST images from an AUROC score of 0.089 to 0.994, based on a PixelCNN++ ... Aug 29, 2023 · ODIN is a preprocessing method for inputs that aims to increase the discriminability of the softmax outputs for In- and Out-of-Distribution data. Implements the Mahalanobis Method. Implements the Energy Score of Energy-based Out-of-distribution Detection. Uses entropy to detect OOD inputs. Implements the MaxLogit method. Oct 28, 2022 · Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from input data through a model. This problem has attracted increasing attention in the area of machine learning. OOD detection has achieved good intrusion detection, fraud detection, system health monitoring, sensor network event detection, and ecosystem interference detection. The method based on deep ... Aug 24, 2022 · We include results for four types of out-of-distribution samples: (1) dataset shift, where we evaluate the model on two other datasets with differences in the acquisition and population patterns (2) transformation shift where we apply artificial transformations to our ID data, (3) diagnostic shift, where we compare Covid-19 to non-Covid ... Mar 3, 2021 · Then, we focus on a certain class of out of distribution problems, their assumptions, and introduce simple algorithms that follow from these assumptions that are able to provide more reliable generalization. A central topic in the thesis is the strong link between discovering the causal structure of the data, finding features that are reliable ... Dec 17, 2019 · The likelihood is dominated by the “background” pixels, whereas the likelihood ratio focuses on the “semantic” pixels and is thus better for OOD detection. Our likelihood ratio method corrects the background effect and significantly improves the OOD detection of MNIST images from an AUROC score of 0.089 to 0.994, based on a PixelCNN++ ... We have summarized the main branches of works for Out-of-Distribution(OOD) Generalization problem, which are classified according to the research focus, including unsupervised representation learning, supervised learning models and optimization methods. For more details, please refer to our survey on OOD generalization. ing data distribution p(x;y). At inference time, given an input x02Xthe goal of OOD detection is to identify whether x0is a sample drawn from p(x;y). 2.2 Types of Distribution Shifts As in (Ren et al.,2019), we assume that any repre-sentation of the input x, ˚(x), can be decomposed into two independent and disjoint components: the background ... Towards Out-Of-Distribution Generalization: A Survey Jiashuo Liu*, Zheyan Shen∗, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, Peng Cui† Department of Computer Science and Technology Tsinghua University [email protected], [email protected], [email protected] Abstract ... Mar 21, 2022 · Most of the existing Out-Of-Distribution (OOD) detection algorithms depend on single input source: the feature, the logit, or the softmax probability. However, the immense diversity of the OOD examples makes such methods fragile. There are OOD samples that are easy to identify in the feature space while hard to distinguish in the logit space and vice versa. Motivated by this observation, we ... out-of-distribution examples, assuming our training set only contains older defendants referred as in-dis-tribution examples. The fractions of data are only for illustrative purposes. See details of in-distribution vs. out-of-distribution setup in §3.2. assistance, human-AI teams should outperform AI alone and human alone (e.g., in accuracy; also Feb 19, 2023 · Abstract. Recently, out-of-distribution (OOD) generalization has attracted attention to the robustness and generalization ability of deep learning based models, and accordingly, many strategies have been made to address different aspects related to this issue. However, most existing algorithms for OOD generalization are complicated and ... CVF Open Access The outputs of an ensemble of networks can be used to estimate the uncertainty of a classifier. At test time, the estimated uncertainty for out-of-distribution samples turns out to be higher than the one for in-distribution samples. 3. level 2. AnvaMiba. Dec 25, 2020 · Out-of-Distribution Detection in Deep Neural Networks Outline:. A bit on OOD. The term “distribution” has slightly different meanings for Language and Vision tasks. Consider a dog... Approaches to Detect OOD instances:. One class of OOD detection techniques is based on thresholding over the ... Towards Out-Of-Distribution Generalization: A Survey Jiashuo Liu*, Zheyan Shen∗, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, Peng Cui† Department of Computer Science and Technology Tsinghua University [email protected], [email protected], [email protected] Abstract ... 1ODIN: Out-of-DIstribution detector for Neural networks [21] failures are therefore often silent in that they do not result in explicit errors in the model. The above issue had been formulated as a problem of detecting whether an input data is from in-distribution (i.e. the training distribution) or out-of-distribution (i.e. a distri- Feb 16, 2022 · To solve this critical problem, out-of-distribution (OOD) generalization on graphs, which goes beyond the I.I.D. hypothesis, has made great progress and attracted ever-increasing attention from the research community. In this paper, we comprehensively survey OOD generalization on graphs and present a detailed review of recent advances in this area. We have summarized the main branches of works for Out-of-Distribution(OOD) Generalization problem, which are classified according to the research focus, including unsupervised representation learning, supervised learning models and optimization methods. For more details, please refer to our survey on OOD generalization. Sep 15, 2022 · The unique contribution of this paper is two-fold, justified by extensive experiments. First, we present a realistic problem setting of OOD task for skin lesion. Second, we propose an approach to target the long-tailed and fine-grained aspects of the problem setting simultaneously to increase the OOD performance. Jul 1, 2021 · In the classification problem, out-of-distribution data means data with classes not included in the training data. Detecting such out-of-distribution data is a critical problem in the stability of an image classification model using deep learning [10 ]. We define wafer map data with a form other than the 16 types of wafer maps corresponding to ... Feb 21, 2022 · Most existing datasets with category and viewpoint labels 13,26,27,28 present two major challenges: (1) lack of control over the distribution of categories and viewpoints, or (2) small size. Thus ... Oct 21, 2021 · Abstract: Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. For instance, in autonomous driving, we would like the driving system to issue an alert and hand over the control to humans when it detects unusual scenes or objects that it has never seen during training time and cannot ... It is well known that fine-tuning leads to better accuracy in-distribution (ID). However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large. On 10 distribution shift datasets Aug 29, 2023 · ODIN is a preprocessing method for inputs that aims to increase the discriminability of the softmax outputs for In- and Out-of-Distribution data. Implements the Mahalanobis Method. Implements the Energy Score of Energy-based Out-of-distribution Detection. Uses entropy to detect OOD inputs. Implements the MaxLogit method. It is well known that fine-tuning leads to better accuracy in-distribution (ID). However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large. On 10 distribution shift datasets 1ODIN: Out-of-DIstribution detector for Neural networks [21] failures are therefore often silent in that they do not result in explicit errors in the model. The above issue had been formulated as a problem of detecting whether an input data is from in-distribution (i.e. the training distribution) or out-of-distribution (i.e. a distri- out-of-distribution examples, assuming our training set only contains older defendants referred as in-dis-tribution examples. The fractions of data are only for illustrative purposes. See details of in-distribution vs. out-of-distribution setup in §3.2. assistance, human-AI teams should outperform AI alone and human alone (e.g., in accuracy; also While out-of-distribution (OOD) generalization, robustness, and detection have been discussed in works related to reducing existential risks from AI (e.g., [Amodei et al., 2016, Hendrycks et al., 2022b]) the truth is that the vast majority of distribution shifts are not directly related to existential risks. Apr 21, 2022 · 👋 Hello @recycie, thank you for your interest in YOLOv5 🚀!Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution. Apr 19, 2023 · Recently, a class of compact and brain-inspired continuous-time recurrent neural networks has shown great promise in modeling autonomous navigation of ground ( 18, 19) and simulated drone vehicles end to end in a closed loop with their environments ( 21 ). These networks are called liquid time-constant (LTC) networks ( 35 ), or liquid networks. We evaluate our method on a diverse set of in- and out-of-distribution dataset pairs. In many settings, our method outperforms other methods by a large margin. The contri-butions of our paper are summarized as follows: • We propose a novel experimental setting and a novel training methodology for out-of-distribution detection in neural networks. While out-of-distribution (OOD) generalization, robustness, and detection have been discussed in works related to reducing existential risks from AI (e.g., [Amodei et al., 2016, Hendrycks et al., 2022b]) the truth is that the vast majority of distribution shifts are not directly related to existential risks. It is well known that fine-tuning leads to better accuracy in-distribution (ID). However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large. On 10 distribution shift datasets marginal distribution of P X,Y for the input variable Xby P 0.Given a test input x ∈X, the problem of out-of-distribution detection can be formulated as a single-sample hypothesis testing task: H 0: x ∼P 0, vs. H 1: x ≁P 0. (1) Here the null hypothesis H 0 implies that the test input x is an in-distribution sample. The goal of examples of 2 in-distribution (from CIFAR-100) and 1 out-of-distribution class (from CIFAR-10). The color coding shows the Mahalanobis outlier score, while the points are projections of embeddings of members of the in-distribution CIFAR-100 classes "sunflowers" (black plus signs) and "turtle" Dec 17, 2019 · The likelihood is dominated by the “background” pixels, whereas the likelihood ratio focuses on the “semantic” pixels and is thus better for OOD detection. Our likelihood ratio method corrects the background effect and significantly improves the OOD detection of MNIST images from an AUROC score of 0.089 to 0.994, based on a PixelCNN++ ... Let Dout denote an out-of-distribution dataset of (xout;y out)pairs where yout 2Y := fK+1;:::;K+Og;Yout\Yin =;. Depending on how different Dout is from Din, we categorize the OOD detection tasks into near-OOD and far-OOD. We first study the scenario where the model is fine-tuned only on the training set D in train without any access to OOD ... May 15, 2022 · 1. We propose an unsupervised method to distinguish in-distribution from out-of-distribution input. The results indicate that the assumptions and methods of outlier and deep anomaly detection are also relevant to the field of out-of-distribution detection. 2. The method works on the basis of an Isolation Forest. Dec 17, 2020 · While deep learning demonstrates its strong ability to handle independent and identically distributed (IID) data, it often suffers from out-of-distribution (OoD) generalization, where the test data come from another distribution (w.r.t. the training one). Designing a general OoD generalization framework to a wide range of applications is challenging, mainly due to possible correlation shift ... Mar 25, 2022 · All solutions mentioned above, such as regularization, multimodality, scaling, and invariant risk minimization, can improve distribution shift and out-of-distribution generalization, ultimately ... Jul 1, 2021 · In general, out-of-distribution data refers to data having a distribution different from that of training data. In the classification problem, out-of-distribution means data with classes that are not included in the training data. In image classification using the deep neural network, the research has been actively conducted to improve the ... Jun 6, 2021 · Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We demonstrate that large-scale pre-trained transformers can significantly improve the state-of-the-art (SOTA) on a range of near OOD tasks across different data modalities. For instance, on CIFAR-100 vs CIFAR-10 OOD detection, we improve the AUROC from 85% (current SOTA) to more than 96% using Vision ... CVF Open Access Apr 16, 2021 · Deep Stable Learning for Out-Of-Distribution Generalization. Xingxuan Zhang, Peng Cui, Renzhe Xu, Linjun Zhou, Yue He, Zheyan Shen. Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise. Therefore, eliminating the impact of ... Jan 22, 2019 · Out-of-distribution detection using an ensemble of self supervised leave-out classifiers A. Vyas, N. Jammalamadaka, X. Zhu, D. Das, B. Kaul, and T. L. Willke, “Out-of-distribution detection using an ensemble of self supervised leave-out classifiers,” in European Conference on Computer Vision, 2018, pp. 560–574. Mar 25, 2022 · All solutions mentioned above, such as regularization, multimodality, scaling, and invariant risk minimization, can improve distribution shift and out-of-distribution generalization, ultimately ... Aug 29, 2023 · ODIN is a preprocessing method for inputs that aims to increase the discriminability of the softmax outputs for In- and Out-of-Distribution data. Implements the Mahalanobis Method. Implements the Energy Score of Energy-based Out-of-distribution Detection. Uses entropy to detect OOD inputs. Implements the MaxLogit method. out-of-distribution examples, assuming our training set only contains older defendants referred as in-dis-tribution examples. The fractions of data are only for illustrative purposes. See details of in-distribution vs. out-of-distribution setup in §3.2. assistance, human-AI teams should outperform AI alone and human alone (e.g., in accuracy; also Let Dout denote an out-of-distribution dataset of (xout;y out)pairs where yout 2Y := fK+1;:::;K+Og;Yout\Yin =;. Depending on how different Dout is from Din, we categorize the OOD detection tasks into near-OOD and far-OOD. We first study the scenario where the model is fine-tuned only on the training set D in train without any access to OOD ... cause of model crash under distribution shifts, they propose to realize out-of-distribution generalization by decorrelat-ing the relevant and irrelevant features. Since there is no extra supervision for separating relevant features from ir-relevant features, a conservative solution is to decorrelate all features.
Nov 26, 2021 · Unsupervised out-of-distribution (U-OOD) detection has recently attracted much attention due its importance in mission-critical systems and broader applicability over its supervised counterpart. Despite this increase in attention, U-OOD methods suffer from important shortcomings. By performing a large-scale evaluation on different benchmarks and image modalities, we show in this work that most ... . Milamalenkov vip
marginal distribution of P X,Y for the input variable Xby P 0.Given a test input x ∈X, the problem of out-of-distribution detection can be formulated as a single-sample hypothesis testing task: H 0: x ∼P 0, vs. H 1: x ≁P 0. (1) Here the null hypothesis H 0 implies that the test input x is an in-distribution sample. The goal of Nov 26, 2021 · Unsupervised out-of-distribution (U-OOD) detection has recently attracted much attention due its importance in mission-critical systems and broader applicability over its supervised counterpart. Despite this increase in attention, U-OOD methods suffer from important shortcomings. By performing a large-scale evaluation on different benchmarks and image modalities, we show in this work that most ... Dec 25, 2020 · Out-of-Distribution Detection in Deep Neural Networks Outline:. A bit on OOD. The term “distribution” has slightly different meanings for Language and Vision tasks. Consider a dog... Approaches to Detect OOD instances:. One class of OOD detection techniques is based on thresholding over the ... cannot deliver reliable reasoning results when facing out-of-distribution samples. Next, even if supervision signals can be properly propagated between the neural and symbolic models, it is still possible that the NN predicts spurious fea-tures, leading to bad generalization performance (an exam-ple is provided in Sec. 6). Feb 21, 2022 · It is well known that fine-tuning leads to better accuracy in-distribution (ID). However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large. On 10 distribution shift datasets (Breeds-Living17, Breeds-Entity30 ... It is well known that fine-tuning leads to better accuracy in-distribution (ID). However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large. On 10 distribution shift datasets May 15, 2022 · 1. We propose an unsupervised method to distinguish in-distribution from out-of-distribution input. The results indicate that the assumptions and methods of outlier and deep anomaly detection are also relevant to the field of out-of-distribution detection. 2. The method works on the basis of an Isolation Forest. While out-of-distribution (OOD) generalization, robustness, and detection have been discussed in works related to reducing existential risks from AI (e.g., [Amodei et al., 2016, Hendrycks et al., 2022b]) the truth is that the vast majority of distribution shifts are not directly related to existential risks. Towards Out-Of-Distribution Generalization: A Survey Jiashuo Liu*, Zheyan Shen∗, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, Peng Cui† Department of Computer Science and Technology Tsinghua University [email protected], [email protected], [email protected] Abstract ... 1ODIN: Out-of-DIstribution detector for Neural networks [21] failures are therefore often silent in that they do not result in explicit errors in the model. The above issue had been formulated as a problem of detecting whether an input data is from in-distribution (i.e. the training distribution) or out-of-distribution (i.e. a distri- cannot deliver reliable reasoning results when facing out-of-distribution samples. Next, even if supervision signals can be properly propagated between the neural and symbolic models, it is still possible that the NN predicts spurious fea-tures, leading to bad generalization performance (an exam-ple is provided in Sec. 6). Aug 24, 2022 · We include results for four types of out-of-distribution samples: (1) dataset shift, where we evaluate the model on two other datasets with differences in the acquisition and population patterns (2) transformation shift where we apply artificial transformations to our ID data, (3) diagnostic shift, where we compare Covid-19 to non-Covid ... Jun 6, 2021 · Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We demonstrate that large-scale pre-trained transformers can significantly improve the state-of-the-art (SOTA) on a range of near OOD tasks across different data modalities. For instance, on CIFAR-100 vs CIFAR-10 OOD detection, we improve the AUROC from 85% (current SOTA) to more than 96% using Vision ... out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maxi-mum softmax probabilities than erroneously classified and out-of-distribution ex-amples, allowing for their detection. We assess performance by defining sev- However, using GANs to detect out-of-distribution instances by measuring the likelihood under the data distribution can fail (Nalisnick et al.,2019), while VAEs often generate ambiguous and blurry explanations. More recently, some re-searchers have argued that using auxiliary generative models in counterfactual generation incurs an engineering ... A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in many real-world machine learning applications. Dec 17, 2020 · While deep learning demonstrates its strong ability to handle independent and identically distributed (IID) data, it often suffers from out-of-distribution (OoD) generalization, where the test data come from another distribution (w.r.t. the training one). Designing a general OoD generalization framework to a wide range of applications is challenging, mainly due to possible correlation shift ... .