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Decoupled Contrastive Learning for Long-Tailed Recognition: Addressing Imbalanced Datasets


Core Concepts
The author addresses biased optimization in long-tailed recognition by decoupling positive samples and leveraging patch-based self distillation. This approach aims to improve performance on imbalanced datasets.
Abstract
The content discusses the challenges of long-tailed recognition due to imbalanced datasets and proposes a solution through Decoupled Supervised Contrastive Loss (DSCL) and Patch-based Self Distillation (PBSD). By optimizing intra-category distance and leveraging shared visual patterns, the method aims to enhance performance across different classes. Experimental results demonstrate the effectiveness of the proposed approach, outperforming recent works on various benchmarks. Key points: Supervised Contrastive Loss (SCL) limitations in long-tailed recognition. Introduction of DSCL to address biased optimization for head and tail classes. Proposal of PBSD to transfer knowledge from head to tail classes using patch-based features. Impactful results showcasing improved accuracy on ImageNet-LT dataset. Comparison with recent methods highlighting superior performance across different datasets.
Stats
Achieves 57.7% top-1 accuracy on ImageNet-LT dataset. Performance boosted to 59.7% with ensemble-based method. Outperforms recent works by 6.5% on long-tailed classification benchmarks.
Quotes
"By optimizing the intra-inter category distance, SCL has achieved impressive performance on balanced datasets." "To improve the performance on long-tailed recognition, this paper addresses those two issues of SCL by decoupling the training objective." "Our method is easy to implement and the code will be released to benefit future research."

Key Insights Distilled From

by Shiyu Xuan,S... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06151.pdf
Decoupled Contrastive Learning for Long-Tailed Recognition

Deeper Inquiries

How can decoupling positive samples lead to a more balanced optimization

Decoupling positive samples can lead to a more balanced optimization by addressing the biased optimization issue that arises in scenarios of long-tailed recognition. In the context of Supervised Contrastive Loss (SCL), treating two types of positive samples equally can result in imbalanced gradients and biased feature learning across different categories. By decoupling the training objective, as proposed in Decoupled Supervised Contrastive Loss (DSCL), the optimization process is restructured to assign different weights to each type of positive sample. This adjustment ensures that the gradient ratio and optimal conditional probability are not influenced by the number of samples in each category. As a result, DSCL prevents biased feature learning and promotes a more balanced intra-category distance optimization across head and tail classes.

What are the implications of leveraging patch-based self distillation in representation learning

Leveraging patch-based self distillation in representation learning has significant implications for enhancing model performance in long-tailed visual recognition tasks. Patch-based features allow for capturing fine-grained visual patterns shared among different instances or classes, enabling a deeper understanding of semantic cues beyond global features. By extracting patch-level information and using it to mine shared visual patterns between head and tail classes, Patch-based Self Distillation (PBSD) facilitates knowledge transfer from well-represented classes to underrepresented ones. This approach helps mitigate the under-representation challenge faced by tail classes, leading to improved overall accuracy on long-tailed datasets.

How does this work contribute to advancing research in long-tailed visual recognition

This work significantly advances research in long-tailed visual recognition by introducing novel techniques like Decoupled Supervised Contrastive Loss (DSCL) and Patch-based Self Distillation (PBSD). The proposed DSCL addresses issues with biased optimization inherent in traditional methods like SCL by decoupling positive samples during training, thus promoting a more balanced intra-category distance optimization across diverse class distributions. Additionally, PBSD leverages patch-level features to capture shared visual patterns among instances from different classes, facilitating knowledge transfer between head and tail categories for enhanced representation learning. By effectively combining these innovative approaches, this work demonstrates superior performance on various long-tailed recognition benchmarks compared to existing methods. The contributions made through DSCL and PBSD pave the way for more effective strategies in handling imbalanced datasets and improving model generalization capabilities across diverse class distributions within real-world applications of computer vision tasks.
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