Cross-Architectural Positive Pairs improve the effectiveness of Self-Supervised Learning

Pranav Singh; Jacopo Cirrone

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arXiv · 2301.12025

Cross-Architectural Positive Pairs improve the effectiveness of Self-Supervised Learning

Pranav Singh·Jacopo Cirrone

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Abstract

Existing self-supervised techniques have extreme computational requirements and suffer a substantial drop in performance with a reduction in batch size or pretraining epochs. This paper presents Cross Architectural - Self Supervision (CASS), a novel self-supervised learning approach that leverages Transformer and CNN simultaneously. Compared to the existing state-of-the-art self-supervised learning approaches, we empirically show that CASS-trained CNNs and Transformers across four diverse datasets gained an average of 3.8% with 1% labeled data, 5.9% with 10% labeled data, and 10.13% with 100% labeled data while taking 69% less time. We also show that CASS is much more robust to changes in batch size and training epochs than existing state-of-the-art self-supervised learning approaches. We have open-sourced our code at https://github.com/pranavsinghps1/CASS.

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BibTeX RIS

Pranav Singh, Jacopo Cirrone. 2023-01-27. Cross-Architectural Positive Pairs improve the effectiveness of Self-Supervised Learning. https://arxiv.org/abs/2301.12025

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