Papers › Visualizing and Understanding Contrastive Learning

Visualizing and Understanding Contrastive Learning

20 Jun 2022arXiv:2206.09753archive 2025-07-28

Fawaz Sammani, Boris Joukovsky, Nikos Deligiannis

Contrastive learning has revolutionized the field of computer vision, learning rich representations from unlabeled data, which generalize well to diverse vision tasks. Consequently, it has become increasingly important to explain these approaches and understand their inner workings mechanisms. Given that contrastive models are trained with interdependent and interacting inputs and aim to learn invariance through data augmentation, the existing methods for explaining single-image systems (e.g., image classification models) are inadequate as they fail to account for these factors and typically assume independent inputs. Additionally, there is a lack of evaluation metrics designed to assess pairs of explanations, and no analytical studies have been conducted to investigate the effectiveness of different techniques used to explaining contrastive learning. In this work, we design visual explanation methods that contribute towards understanding similarity learning tasks from pairs of images. We further adapt existing metrics, used to evaluate visual explanations of image classification systems, to suit pairs of explanations and evaluate our proposed methods with these metrics. Finally, we present a thorough analysis of visual explainability methods for contrastive learning, establish their correlation with downstream tasks and demonstrate the potential of our approaches to investigate their merits and drawbacks.

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annonymoussubmission/explain-cl officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Contrastive Learning Data Augmentation Explainable artificial intelligence Self-Supervised Learning image-classification

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1x1 Convolution Average Pooling Barlow Twins Batch Normalization Bottleneck Residual Block ColorJitter Convolution Dense Connections Feedforward Network Global Average Pooling Kaiming Initialization Max Pooling NT-Xent Random Gaussian Blur Random Resized Crop ReLU Residual Block Residual Connection SimCLR

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