Extremely Simple Activation Shaping for Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2209.09858v2
• Date: Mon, 1 May 2023 22:27:08 GMT
• Title: Extremely Simple Activation Shaping for Out-of-Distribution Detection
• Authors: Andrija Djurisic, Nebojsa Bozanic, Arjun Ashok, Rosanne Liu
• Abstract summary: Out-of-distribution (OOD) detection is an important area that stress-tests a model's ability to handle unseen situations. Existing OOD detection methods either incur extra training steps, additional data or make nontrivial modifications to the trained network. We propose an extremely simple, post-hoc, on-the-fly activation shaping method, ASH, where a large portion of a sample's activation at a late layer is removed. Experiments show that such a simple treatment enhances in-distribution and out-of-distribution distinction so as to allow state-of-the-art OOD
• Score: 10.539058676970267
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The separation between training and deployment of machine learning models implies that not all scenarios encountered in deployment can be anticipated during training, and therefore relying solely on advancements in training has its limits. Out-of-distribution (OOD) detection is an important area that stress-tests a model's ability to handle unseen situations: Do models know when they don't know? Existing OOD detection methods either incur extra training steps, additional data or make nontrivial modifications to the trained network. In contrast, in this work, we propose an extremely simple, post-hoc, on-the-fly activation shaping method, ASH, where a large portion (e.g. 90%) of a sample's activation at a late layer is removed, and the rest (e.g. 10%) simplified or lightly adjusted. The shaping is applied at inference time, and does not require any statistics calculated from training data. Experiments show that such a simple treatment enhances in-distribution and out-of-distribution distinction so as to allow state-of-the-art OOD detection on ImageNet, and does not noticeably deteriorate the in-distribution accuracy. Video, animation and code can be found at: https://andrijazz.github.io/ash

Related papers

• Rethinking Classifier Re-Training in Long-Tailed Recognition: A Simple Logits Retargeting Approach [102.0769560460338]
  We develop a simple logits approach (LORT) without the requirement of prior knowledge of the number of samples per class. Our method achieves state-of-the-art performance on various imbalanced datasets, including CIFAR100-LT, ImageNet-LT, and iNaturalist 2018.
  arXiv  Detail & Related papers  (2024-03-01T03:27:08Z)
• Using Semantic Information for Defining and Detecting OOD Inputs [3.9577682622066264]
  Out-of-distribution (OOD) detection has received some attention recently. We demonstrate that the current detectors inherit the biases in the training dataset. This can render the current OOD detectors impermeable to inputs lying outside the training distribution but with the same semantic information. We perform OOD detection on semantic information extracted from the training data of MNIST and COCO datasets.
  arXiv  Detail & Related papers  (2023-02-21T21:31:20Z)
• CAFA: Class-Aware Feature Alignment for Test-Time Adaptation [50.26963784271912]
  Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time. We propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously encourages a model to learn target representations in a class-discriminative manner.
  arXiv  Detail & Related papers  (2022-06-01T03:02:07Z)
• Model2Detector:Widening the Information Bottleneck for Out-of-Distribution Detection using a Handful of Gradient Steps [12.263417500077383]
  Out-of-distribution detection is an important capability that has long eluded vanilla neural networks. Recent advances in inference-time out-of-distribution detection help mitigate some of these problems. We show how our method consistently outperforms the state-of-the-art in detection accuracy on popular image datasets.
  arXiv  Detail & Related papers  (2022-02-22T23:03:40Z)
• Agree to Disagree: Diversity through Disagreement for Better Transferability [54.308327969778155]
  We propose D-BAT (Diversity-By-disAgreement Training), which enforces agreement among the models on the training data. We show how D-BAT naturally emerges from the notion of generalized discrepancy.
  arXiv  Detail & Related papers  (2022-02-09T12:03:02Z)
• Tracking the risk of a deployed model and detecting harmful distribution shifts [105.27463615756733]
  In practice, it may make sense to ignore benign shifts, under which the performance of a deployed model does not degrade substantially. We argue that a sensible method for firing off a warning has to both (a) detect harmful shifts while ignoring benign ones, and (b) allow continuous monitoring of model performance without increasing the false alarm rate.
  arXiv  Detail & Related papers  (2021-10-12T17:21:41Z)
• Distribution Mismatch Correction for Improved Robustness in Deep Neural Networks [86.42889611784855]
  normalization methods increase the vulnerability with respect to noise and input corruptions. We propose an unsupervised non-parametric distribution correction method that adapts the activation distribution of each layer. In our experiments, we empirically show that the proposed method effectively reduces the impact of intense image corruptions.
  arXiv  Detail & Related papers  (2021-10-05T11:36:25Z)
• Self-Damaging Contrastive Learning [92.34124578823977]
  Unlabeled data in reality is commonly imbalanced and shows a long-tail distribution. This paper proposes a principled framework called Self-Damaging Contrastive Learning to automatically balance the representation learning without knowing the classes. Our experiments show that SDCLR significantly improves not only overall accuracies but also balancedness.
  arXiv  Detail & Related papers  (2021-06-06T00:04:49Z)
• Deep Active Learning in Remote Sensing for data efficient Change Detection [26.136331738529243]
  We investigate active learning in the context of deep neural network models for change detection and map updating. In active learning, one starts from a minimal set of training examples and progressively chooses informative samples annotated by a user. We show that active learning successfully finds highly informative samples and automatically balances the training distribution.
  arXiv  Detail & Related papers  (2020-08-25T17:58:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.