HCSC: Hierarchical Contrastive Selective Coding (2202.00455v4)

Abstract: Hierarchical semantic structures naturally exist in an image dataset, in which several semantically relevant image clusters can be further integrated into a larger cluster with coarser-grained semantics. Capturing such structures with image representations can greatly benefit the semantic understanding on various downstream tasks. Existing contrastive representation learning methods lack such an important model capability. In addition, the negative pairs used in these methods are not guaranteed to be semantically distinct, which could further hamper the structural correctness of learned image representations. To tackle these limitations, we propose a novel contrastive learning framework called Hierarchical Contrastive Selective Coding (HCSC). In this framework, a set of hierarchical prototypes are constructed and also dynamically updated to represent the hierarchical semantic structures underlying the data in the latent space. To make image representations better fit such semantic structures, we employ and further improve conventional instance-wise and prototypical contrastive learning via an elaborate pair selection scheme. This scheme seeks to select more diverse positive pairs with similar semantics and more precise negative pairs with truly distinct semantics. On extensive downstream tasks, we verify the superior performance of HCSC over state-of-the-art contrastive methods, and the effectiveness of major model components is proved by plentiful analytical studies. We build a comprehensive model zoo in Sec. D. Our source code and model weights are available at https://github.com/gyfastas/HCSC

• The paper presents hierarchical prototypes that capture multi-layer semantic structures using a bottom-up K-means approach.
• The paper integrates instance-wise and prototypical contrastive learning with dynamic pair selection to refine semantic alignment.
• The framework achieves significant results, including a 69.2% top-1 accuracy on ImageNet, outperforming several state-of-the-art methods.

Hierarchical Contrastive Selective Coding: Enhancing Representation Learning

The paper introduces a novel representation learning framework titled Hierarchical Contrastive Selective Coding (HCSC), which aims to capture hierarchical semantic structures in datasets for improved representation learning. This approach addresses limitations in existing contrastive learning methods that primarily focus on local or single-layer semantic structures, thereby missing out on the potential benefits of hierarchical information.

Core Ideas

HCSC encapsulates the following key innovations:

1. Hierarchical Prototypes: The framework employs hierarchical prototypes to represent semantic structures within the latent space. These prototypes help capture relationships among various semantic clusters, informed by a bottom-up hierarchical K-means approach. This approach distinguishes HCSC from prior methods that typically represent semantic structures using a single hierarchy.
2. Instance-wise and Prototypical Contrastive Learning: The method integrates both instance-wise and prototypical contrastive learning schemes to exploit local as well as global semantic structures. Instance-wise contrastive learning bundles similar instances together in the latent space. In contrast, prototypical contrastive learning concentrates on forming compact image representations around designated cluster centers, thereby capturing semantic structures elaborated by a single hierarchy.
3. Selective Pairing for Contrastive Learning: At the core of HCSC is an advanced pair selection scheme that enriches instance-wise and prototypical learning processes. By dynamically selecting positive pairs with similar semantics and more precise negative pairs that reflect truly distinct semantics, the approach ensures the improved structural validity of learned representations. This scheme leverages the semantic hierarchy to refine how positive and negative pairs are chosen, leading to better-aligned semantic learning objectives.

Empirical Evaluation

The proposed HCSC framework significantly outperforms various state-of-the-art methods on downstream tasks, notably those including complex hierarchical data. Its superior efficacy was demonstrated across several benchmarks, including linear classification, KNN evaluation, semi-supervised learning, transfer learning, and clustering evaluations in diverse settings such as object classification and detection, both with and without multi-crop augmentation. Notably, HCSC achieved superior accuracy in linear evaluation tasks, evidenced by a 69.2% top-1 accuracy on ImageNet with 256 batch size, setting it apart from existing methodologies like SimCLR and MoCo v2.

Implications and Future Directions

HCSC's effective use of hierarchical semantics for crafting informative image representations has practical implications for tasks involving complex hierarchical data structures. Notably, this framework is well-positioned to enhance various machine learning applications from image recognition to more intricate data processing tasks across multiple domains.

Theoretically, HCSC introduces new possibilities for incorporating multi-layered semantic structures into learning models, setting a precedent for extensions of contrastive learning algorithms that harness hierarchical data insights. Future work could explore integrating the hierarchical prototypes into downstream applications, potentially bridging gaps in current knowledge representation and application.

In conclusion, HCSC represents a significant advancement in self-supervised image representation learning by innovatively leveraging hierarchical prototypes to enhance semantic capturing capabilities. This framework opens up new research trajectories around aligning data semantics with learning objectives, particularly within the rapidly evolving landscape of machine intelligence.