Core Concepts
Adaptive batch selection improves multi-label classification by focusing on hard and imbalanced samples.
Abstract
The content discusses the importance of adaptive batch selection in multi-label classification. It introduces a novel approach that prioritizes hard samples related to minority labels, improving convergence and performance. The method combines binary cross-entropy loss with global and local imbalance weights, addressing class imbalance issues. Experiments on various datasets demonstrate the effectiveness of the adaptive batch selection strategy.
Introduction
Deep learning success in multi-label classification.
Class imbalance challenges in multi-label data.
Proposed Method
Rank-based batch selection.
Class imbalance aware weighting.
Incorporation of quantization index.
Adaptive batch selection with label correlations.
Experiments and Analysis
Evaluation metrics and datasets.
Comparative results of batch selection strategies.
Convergence analysis from different perspectives.
Investigation of loss metrics.
Parameter analysis impact on the method.
Conclusion
Proposal of a novel adaptive batch selection strategy.
Improved convergence and performance in multi-label classification.
Stats
"The adaptive batch selection method outperforms random selection with statistical significance."
"The adaptive batch selection significantly enhances the performance of deep learning models."
Quotes
"Samples associated with minority labels tend to induce greater losses."
"Adaptive batch selection consistently outperformed random selection."