ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations

• URL: http://arxiv.org/abs/2211.01866v1
• Date: Thu, 3 Nov 2022 14:56:32 GMT
• Title: ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations
• Authors: Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero, Ivan Evtimov, Caner Hazirbas, Nicolas Ballas, Pascal Vincent, Michal Drozdzal, David Lopez-Paz, Mark Ibrahim
• Abstract summary: We introduce ImageNet-X, a set of sixteen human annotations of factors such as pose, background, or lighting. We investigate 2,200 current recognition models and study the types of mistakes as a function of model's architecture. We find models have consistent failure modes across ImageNet-X categories.
• Score: 36.348968311668564
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning vision systems are widely deployed across applications where reliability is critical. However, even today's best models can fail to recognize an object when its pose, lighting, or background varies. While existing benchmarks surface examples challenging for models, they do not explain why such mistakes arise. To address this need, we introduce ImageNet-X, a set of sixteen human annotations of factors such as pose, background, or lighting the entire ImageNet-1k validation set as well as a random subset of 12k training images. Equipped with ImageNet-X, we investigate 2,200 current recognition models and study the types of mistakes as a function of model's (1) architecture, e.g. transformer vs. convolutional, (2) learning paradigm, e.g. supervised vs. self-supervised, and (3) training procedures, e.g., data augmentation. Regardless of these choices, we find models have consistent failure modes across ImageNet-X categories. We also find that while data augmentation can improve robustness to certain factors, they induce spill-over effects to other factors. For example, strong random cropping hurts robustness on smaller objects. Together, these insights suggest to advance the robustness of modern vision models, future research should focus on collecting additional data and understanding data augmentation schemes. Along with these insights, we release a toolkit based on ImageNet-X to spur further study into the mistakes image recognition systems make.

Related papers

• Reinforcing Pre-trained Models Using Counterfactual Images [54.26310919385808]
  This paper proposes a novel framework to reinforce classification models using language-guided generated counterfactual images. We identify model weaknesses by testing the model using the counterfactual image dataset. We employ the counterfactual images as an augmented dataset to fine-tune and reinforce the classification model.
  arXiv  Detail & Related papers  (2024-06-19T08:07:14Z)
• Identifying and Mitigating Model Failures through Few-shot CLIP-aided Diffusion Generation [65.268245109828]
  We propose an end-to-end framework to generate text descriptions of failure modes associated with spurious correlations. These descriptions can be used to generate synthetic data using generative models, such as diffusion models. Our experiments have shown remarkable textbfimprovements in accuracy ($sim textbf21%$) on hard sub-populations.
  arXiv  Detail & Related papers  (2023-12-09T04:43:49Z)
• ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing [45.14977000707886]
  Higher accuracy on ImageNet usually leads to better robustness against different corruptions. We create a toolkit for object editing with controls of backgrounds, sizes, positions, and directions. We evaluate the performance of current deep learning models, including both convolutional neural networks and vision transformers.
  arXiv  Detail & Related papers  (2023-03-30T02:02:32Z)
• Diverse, Difficult, and Odd Instances (D2O): A New Test Set for Object Classification [47.64219291655723]
  We introduce a new test set, called D2O, which is sufficiently different from existing test sets. Our dataset contains 8,060 images spread across 36 categories, out of which 29 appear in ImageNet. The best Top-1 accuracy on our dataset is around 60% which is much lower than 91% best Top-1 accuracy on ImageNet.
  arXiv  Detail & Related papers  (2023-01-29T19:58:32Z)
• ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints [42.64942578228025]
  We propose a novel method called ViewFool to find adversarial viewpoints that mislead visual recognition models. By encoding real-world objects as neural radiance fields (NeRF), ViewFool characterizes a distribution of diverse adversarial viewpoints.
  arXiv  Detail & Related papers  (2022-10-08T03:06:49Z)
• Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision [38.22842778742829]
  Discriminative self-supervised learning allows training models on any random group of internet images. We train models on billions of random images without any data pre-processing or prior assumptions about what we want the model to learn. We extensively study and validate our model performance on over 50 benchmarks including fairness, to distribution shift, geographical diversity, fine grained recognition, image copy detection and many image classification datasets.
  arXiv  Detail & Related papers  (2022-02-16T22:26:47Z)
• Automated Cleanup of the ImageNet Dataset by Model Consensus, Explainability and Confident Learning [0.0]
  ImageNet was the backbone of various convolutional neural networks (CNNs) trained on ILSVRC12Net. This paper describes automated applications based on model consensus, explainability and confident learning to correct labeling mistakes. The ImageNet-Clean improves the model performance by 2-2.4 % for SqueezeNet and EfficientNet-B0 models.
  arXiv  Detail & Related papers  (2021-03-30T13:16:35Z)
• Contemplating real-world object classification [53.10151901863263]
  We reanalyze the ObjectNet dataset recently proposed by Barbu et al. containing objects in daily life situations. We find that applying deep models to the isolated objects, rather than the entire scene as is done in the original paper, results in around 20-30% performance improvement.
  arXiv  Detail & Related papers  (2021-03-08T23:29:59Z)

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.