← 研踪 PaperTrailCVF · CVPR 2026:cvf:5aed82cd95ca5e2abba14230c9b75107 · 记录 #1
AdaPrior: Bayesian-Inspired Adaptive Prior Correction for Long-Tailed Continual Learning
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 04:03
摘要
Long-Tail Class Incremental Learning (LTCIL) combines two fundamental challenges: catastrophic forgetting of past tasks and severe class imbalance. Existing approaches mitigate one challenge at a time, through rehearsal, reweighting, or classifier alignment, but they typically assume static priors and rely on multi-stage training. In contrast, we propose AdaPrior, a simple Bayesian framework that treats LTCIL as a problem of dynamic prior misalignment. Our key idea is to estimate model-induced priors online via an exponential moving average and use them for (i) debiasing during training (AdaPrior Loss), and (ii) lightweight post-hoc correction at inference. The combined approach unifies loss-level and inference-level debiasing without additional stages or heavy computation. We provide theoretical analysis showing that AdaPrior's prior estimator converges to the true model prior and that its logit adjustment yields well calibrated posteriors under mild assumptions. Extensive experiments on CIFAR100-LT, Food-101-LT, ImageNet-LT-subset, and iNaturalist18-subset demonstrate consistent gains over recent LTCIL baselines. Beyond accuracy, AdaPrior improves calibration, and forgetting curves, making it a practical and scalable solution for long-tail continual learning.
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记录 #1 · 2026 · 最新观测版本
AdaPrior: Bayesian-Inspired Adaptive Prior Correction for Long-Tailed Continual Learning
Long-Tail Class Incremental Learning (LTCIL) combines two fundamental challenges: catastrophic forgetting of past tasks and severe class imbalance. Existing approaches mitigate one challenge at a time, through rehearsal, reweighting, or classifier alignment, but they typically assume static priors and rely on multi-stage training. In contrast, we propose AdaPrior, a simple Bayesian framework that treats LTCIL as a problem of dynamic prior misalignment. Our key idea is to estimate model-induced priors online via an exponential moving average and use them for (i) debiasing during training (AdaPrior Loss), and (ii) lightweight post-hoc correction at inference. The combined approach unifies loss-level and inference-level debiasing without additional stages or heavy computation. We provide theoretical analysis showing that AdaPrior's prior estimator converges to the true model prior and that its logit adjustment yields well calibrated posteriors under mild assumptions. Extensive experiments on CIFAR100-LT, Food-101-LT, ImageNet-LT-subset, and iNaturalist18-subset demonstrate consistent gains over recent LTCIL baselines. Beyond accuracy, AdaPrior improves calibration, and forgetting curves, making it a practical and scalable solution for long-tail continual learning.
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