arXiv:2606.05740v1 Announce Type: new Abstract: Deep neural networks trained under severe class imbalance often exhibit degraded performance, typically attributed to statistical bias. In this work, we identify a complementary optimization-level pathology: inter-class gradient interference within shared representations, where gradients from majority classes suppress minority-class learning. To analyze this phenomenon, we introduce a diagnostic framework based on layer-wise gradient flow analysis and a Gradient Conflict Matrix, which quantifies interference using cosine similarity between class-
This research addresses a fundamental challenge in deep learning optimization that becomes increasingly critical as AI models are deployed in real-world, imbalanced data environments.
Improving AI performance under class imbalance is crucial for robust and fair applications across diverse fields, impacting decision-making systems and resource allocation.
This research provides a new diagnostic framework and potential mitigation strategy for a known AI limitation, offering a pathway to more reliable and equitable AI systems.
- · AI researchers
- · Developers of AI systems for critical applications
- · Sectors reliant on AI for minority class detection (e.g., medical diagnostics, f
- · Systems that fail to adapt to class-imbalance solutions
- · Current methods heavily biased towards majority classes
Improved performance of deep neural networks in scenarios with imbalanced datasets.
Reduced bias in AI applications, leading to more equitable outcomes in areas like credit scoring or disease diagnosis.
Accelerated adoption of AI in sensitive fields where fairness and accuracy for all classes are paramount.
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Read at arXiv cs.AI