Shortcut Learning Susceptibility in Vision Classifiers

• URL: http://arxiv.org/abs/2502.09150v1
• Date: Thu, 13 Feb 2025 10:25:52 GMT
• Title: Shortcut Learning Susceptibility in Vision Classifiers
• Authors: Pirzada Suhail, Amit Sethi,
• Abstract summary: Shortcut learning is where machine learning models exploit spurious correlations in data instead of capturing meaningful features. This phenomenon is prevalent across various machine learning applications, including vision, natural language processing, and speech recognition. We systematically evaluate these architectures by introducing deliberate shortcuts into the dataset that are positionally correlated with class labels.
• Score: 3.004632712148892
• License:
• Abstract: Shortcut learning, where machine learning models exploit spurious correlations in data instead of capturing meaningful features, poses a significant challenge to building robust and generalizable models. This phenomenon is prevalent across various machine learning applications, including vision, natural language processing, and speech recognition, where models may find unintended cues that minimize training loss but fail to capture the underlying structure of the data. Vision classifiers such as Convolutional Neural Networks (CNNs), Multi-Layer Perceptrons (MLPs), and Vision Transformers (ViTs) leverage distinct architectural principles to process spatial and structural information, making them differently susceptible to shortcut learning. In this study, we systematically evaluate these architectures by introducing deliberate shortcuts into the dataset that are positionally correlated with class labels, creating a controlled setup to assess whether models rely on these artificial cues or learn actual distinguishing features. We perform both quantitative evaluation by training on the shortcut-modified dataset and testing them on two different test sets -- one containing the same shortcuts and another without them -- to determine the extent of reliance on shortcuts. Additionally, qualitative evaluation is performed by using network inversion-based reconstruction techniques to analyze what the models internalize in their weights, aiming to reconstruct the training data as perceived by the classifiers. We evaluate shortcut learning behavior across multiple benchmark datasets, including MNIST, Fashion-MNIST, SVHN, and CIFAR-10, to compare the susceptibility of different vision classifier architectures to shortcut reliance and assess their varying degrees of sensitivity to spurious correlations.

Related papers

• Differentiation and Specialization of Attention Heads via the Refined Local Learning Coefficient [0.49478969093606673]
  We introduce refined variants of the Local Learning Coefficient (LLC), a measure of model complexity grounded in singular learning theory. We study the development of internal structure in transformer language models during training.
  arXiv  Detail & Related papers  (2024-10-03T20:51:02Z)
• Learning Object-Centric Representation via Reverse Hierarchy Guidance [73.05170419085796]
  Object-Centric Learning (OCL) seeks to enable Neural Networks to identify individual objects in visual scenes. RHGNet introduces a top-down pathway that works in different ways in the training and inference processes. Our model achieves SOTA performance on several commonly used datasets.
  arXiv  Detail & Related papers  (2024-05-17T07:48:27Z)
• Multi-Modal Prompt Learning on Blind Image Quality Assessment [65.0676908930946]
  Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Traditional methods, hindered by a lack of sufficiently annotated data, have employed the CLIP image-text pretraining model as their backbone to gain semantic awareness. Recent approaches have attempted to address this mismatch using prompt technology, but these solutions have shortcomings. This paper introduces an innovative multi-modal prompt-based methodology for IQA.
  arXiv  Detail & Related papers  (2024-04-23T11:45:32Z)
• A Probabilistic Model Behind Self-Supervised Learning [53.64989127914936]
  In self-supervised learning (SSL), representations are learned via an auxiliary task without annotated labels. We present a generative latent variable model for self-supervised learning. We show that several families of discriminative SSL, including contrastive methods, induce a comparable distribution over representations.
  arXiv  Detail & Related papers  (2024-02-02T13:31:17Z)
• Learning Deep Representations via Contrastive Learning for Instance Retrieval [11.736450745549792]
  This paper makes the first attempt that tackles the problem using instance-discrimination based contrastive learning (CL) In this work, we approach this problem by exploring the capability of deriving discriminative representations from pre-trained and fine-tuned CL models.
  arXiv  Detail & Related papers  (2022-09-28T04:36:34Z)
• CONVIQT: Contrastive Video Quality Estimator [63.749184706461826]
  Perceptual video quality assessment (VQA) is an integral component of many streaming and video sharing platforms. Here we consider the problem of learning perceptually relevant video quality representations in a self-supervised manner. Our results indicate that compelling representations with perceptual bearing can be obtained using self-supervised learning.
  arXiv  Detail & Related papers  (2022-06-29T15:22:01Z)
• Cross-modal Representation Learning for Zero-shot Action Recognition [67.57406812235767]
  We present a cross-modal Transformer-based framework, which jointly encodes video data and text labels for zero-shot action recognition (ZSAR) Our model employs a conceptually new pipeline by which visual representations are learned in conjunction with visual-semantic associations in an end-to-end manner. Experiment results show our model considerably improves upon the state of the arts in ZSAR, reaching encouraging top-1 accuracy on UCF101, HMDB51, and ActivityNet benchmark datasets.
  arXiv  Detail & Related papers  (2022-05-03T17:39:27Z)
• ScatSimCLR: self-supervised contrastive learning with pretext task regularization for small-scale datasets [5.2424255020469595]
  We consider a problem of self-supervised learning for small-scale datasets based on contrastive loss between multiple views of the data. We argue that the number of parameters of the whole system and the number of views can be considerably reduced while preserving the same classification accuracy.
  arXiv  Detail & Related papers  (2021-08-31T15:58:45Z)
• Layer-wise Analysis of a Self-supervised Speech Representation Model [26.727775920272205]
  Self-supervised learning approaches have been successful for pre-training speech representation models. Not much has been studied about the type or extent of information encoded in the pre-trained representations themselves.
  arXiv  Detail & Related papers  (2021-07-10T02:13:25Z)
• A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes [42.57604833160855]
  We present a visual analytics framework for the multi-level exploration of the transfer learning processes when training deep neural networks. Our framework establishes a multi-aspect design to explain how the learned knowledge from the existing model is transferred into the new learning task when training deep neural networks.
  arXiv  Detail & Related papers  (2020-09-15T05:59:00Z)
• Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision [57.14468881854616]
  We propose an auxiliary training objective that improves the generalization capabilities of neural networks. We use pairs of minimally-different examples with different labels, a.k.a counterfactual or contrasting examples, which provide a signal indicative of the underlying causal structure of the task. Models trained with this technique demonstrate improved performance on out-of-distribution test sets.
  arXiv  Detail & Related papers  (2020-04-20T02:47:49Z)

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.