arXiv Machine Learning
Local Reference Geometry Residual Augmentation for Imbalanced Time Series Classification
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arXiv Machine Learning
5d ago
Beyond Scarcity: When Encoder Adaptation Is Worthwhile
The paper introduces a controlled evaluation framework to determine when encoder adaptation is beneficial after correcting for class imbalance in long‑tailed recognition tasks. By separating unique support from representation allocation and comparing classifier‑only versus full‑network continuations, the study shows that encoder updates become worthwhile only when representation allocation is highly imbalanced, a transition that persists across various corrections and scales to ImageNet. The framework uses stage‑specific controls, multiple objectives, whole‑distribution metrics, and hierarchical inference to robustly estimate incremental encoder value.
arXiv Machine Learning
Jul 14
A Strong Balanced-Softmax Classifier-Retraining Baseline for Long-Tailed Recognition
arXiv:2607. 09832v1 Announce Type: new Abstract: Long-tailed recognition methods often modify losses, margins, or representations to reduce the dominance of frequent classes.
arXiv Machine Learning
Jul 14
Vertical Fusion: Condensing Internal Representations for Robust ViT Classification
arXiv:2607. 10391v1 Announce Type: cross Abstract: Despite exposing rich intermediate representations, Vision Transformers (ViTs) are almost exclusively utilized as black-box feature extractors, where only the last layer is considered for downstream tasks.
arXiv AI
Jul 21
DADIR: Density-Aware Data-level Imbalanced Regression Framework
arXiv:2607. 17178v1 Announce Type: cross Abstract: Imbalanced learning addresses predictive modeling problems with underrepresented regions of the data distribution.
arXiv AI
Jun 16
Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques
arXiv:2509. 07605v2 Announce Type: replace-cross Abstract: Class imbalance poses a significant challenge to supervised classification, particularly in critical domains like medical diagnostics and anomaly detection where minority class instances are rare.
arXiv AI
Oct 27
A Canary in the Neural Mine: Catching an Image Classifier's Silent, Confident Failures
arXiv:2610.33063v1 Announce Type: cross Abstract: Deployed vision models auto-accept confident predictions, so high-confidence errors propagate silently. Maximum Softmax Probability (MSP), the standa...