Fine-Grained Representation Learning via Multi-Level Contrastive Learning without Class Priors

Houwang Jiang; Zhuxian Liu; Guodong Liu; Xiaolong Liu; Shihua Zhan

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arXiv · 2409.04867

Fine-Grained Representation Learning via Multi-Level Contrastive Learning without Class Priors

Houwang Jiang·Zhuxian Liu·Guodong Liu·Xiaolong Liu·Shihua Zhan

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Abstract

Recent advances in unsupervised representation learning often rely on knowing the number of classes to improve feature extraction and clustering. However, this assumption raises an important question: is the number of classes always necessary, and do class labels fully capture the fine-grained features within the data? In this paper, we propose Contrastive Disentangling (CD), a framework designed to learn representations without relying on class priors. CD leverages a multi-level contrastive learning strategy, integrating instance-level and feature-level contrastive losses with a normalized entropy loss to capture semantically rich and fine-grained representations. Specifically, (1) the instance-level contrastive loss separates feature representations across samples; (2) the feature-level contrastive loss promotes independence among feature heads; and (3) the normalized entropy loss ensures feature diversity and prevents feature collapse. Extensive experiments on CIFAR-10, CIFAR-100, STL-10, and ImageNet-10 demonstrate that CD outperforms existing methods in scenarios where class information is unavailable or ambiguous. The code is available at https://github.com/Hoper-J/Contrastive-Disentangling.

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Houwang Jiang, Zhuxian Liu, Guodong Liu, Xiaolong Liu, Shihua Zhan. 2024-09-07. Fine-Grained Representation Learning via Multi-Level Contrastive Learning without Class Priors. https://arxiv.org/abs/2409.04867

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