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Papers/A Framework For Contrastive Self-Supervised Learning And D...

A Framework For Contrastive Self-Supervised Learning And Designing A New Approach

William Falcon, Kyunghyun Cho

2020-08-31Image ClassificationSelf-Supervised LearningData Augmentation
PaperPDFCodeCode

Abstract

Contrastive self-supervised learning (CSL) is an approach to learn useful representations by solving a pretext task that selects and compares anchor, negative and positive (APN) features from an unlabeled dataset. We present a conceptual framework that characterizes CSL approaches in five aspects (1) data augmentation pipeline, (2) encoder selection, (3) representation extraction, (4) similarity measure, and (5) loss function. We analyze three leading CSL approaches--AMDIM, CPC, and SimCLR--, and show that despite different motivations, they are special cases under this framework. We show the utility of our framework by designing Yet Another DIM (YADIM) which achieves competitive results on CIFAR-10, STL-10 and ImageNet, and is more robust to the choice of encoder and the representation extraction strategy. To support ongoing CSL research, we release the PyTorch implementation of this conceptual framework along with standardized implementations of AMDIM, CPC (V2), SimCLR, BYOL, Moco (V2) and YADIM.

Results

TaskDatasetMetricValueModel
Image ClassificationSTL-10Percentage correct93.8AMDIM
Image ClassificationSTL-10Percentage correct92.15YADIM
Image ClassificationSTL-10Percentage correct78.36CPC†
Image ClassificationSTL-10Percentage correct61Simulated Fixations

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