Jun 2026· arXiv.org· Vol abs/2606.28654· 0 citations· 45 references
Computer Science
TL;DR
This work proposes FedLAS: Feature-Modulated Bidirectional Label Smoothing, a plug-and-play algorithm for label smoothing-based losses that consistently improves calibration compared to modern baselines, reducing Expected Calibration Error (ECE) and Adaptive ECE while maintaining Top-1 accuracy.
Abstract
Deep Neural Network (DNN) classifiers suffer from poor calibration when their softmax outputs (predictive confidence) deviate from the empirical likelihoods. This manifests itself as either overconfident incorrect predictions or under-confident correct predictions. Label smoothing (LS) enhances model calibration by introducing entropy regularization during training through redistributing probability mass from the ground-truth label to the remaining classes. LS, including Margin-based LS (MbLS), have restrictive assumptions: they rely on predefined, uniform smoothing rules and only tackle overconfidence. In reality, samples exhibit diverse characteristics, such as difficulty/ambiguity, that interact with the evolving nature of the model being trained. In training, samples may have various degrees of under- or overconfidence. To overcome this, a mechanism that identifies the specific confidence state of each sample and determines the appropriate degree of smoothing in each training step is needed, tailoring the adjustment to the individual sample. We propose FedLAS: Feature-Modulated Bidirectional Label Smoothing, a plug-and-play algorithm for label smoothing-based losses. In FedLAS, we introduce a Feature Norm-based Confidence Indicator (NCI) to control smoothing and a Bidirectional Calibration Gating (BCG) module to detect both over and under-confidence. Our algorithm can be integrated with LS and MbLS based losses when applied to standard DNNs, enhancing performance. Extensive experiments on standard and fine-grained high-resolution vision benchmarks show that FedLAS consistently improves calibration compared to modern baselines, reducing Expected Calibration Error (ECE) and Adaptive ECE while maintaining Top-1 accuracy. Code: github.com/nadarasarbahavan/FEDLAS
Extensive experiments conducted on UCI and KEEL benchmark datasets demonstrate the superiority of the proposed IF-dRVFL and IF-edRVFL models over existing SOTA fuzzy and non-fuzzy approaches.
M. Sajid, A. Quadir, A. Rahaman et al.· 0 citations
Deep neural networks often suffer significant accuracy degradation when exposed to real-world image corruptions and distribution shifts. To overcome the limitations of fixed, input-agnostic test-time augmentation (TTA), an adaptive framework is proposed that learns per-sample transformations via reinforcement learning. Augmentation selection is cast as a Markov decision process and proximal policy optimization (PPO) agents are trained to choose sample-specific transforms under a composite reward combining classifier confidence gains with a self-consistency KL-divergence penalty on the model's own softmax outputs, thereby preserving overall belief stability. On clean CIFAR-10 (1 000 samples), the adaptive ensemble raises accuracy from 88.5% (baseline) and 87.3% (static TTA) to 90.0% (+1.5 pp). On CIFAR-10-C (15 corruptions × 5 severities; 1 000 images per condition), pooled top-1 accuracy improves from 75.7% (baseline) and 74.3% (static TTA) to 76.4% (+0.7 pp), and exceeds a TENT entropy-minimization baseline (75.9%) while operating in a strictly label-free regime that updates no model weights. Per-corruption gains are consistently positive across noise, blur, weather, and compression distortions, with the adaptive policy outperforming TENT on texture and compression corruptions where input-space transforms are most effective. These findings demonstrate that learned, per-sample augmentation policies improve robustness and reliability of deep vision models under diverse image conditions, against a strong baseline classifier.
Tushar Mittal, A. Dubey, Dharmender Saini et al.· Scientific Reports· 0 citations
HIFN-T is presented, a framework extending the Variational Information Bottleneck through four jointly learnable per-layer parameters: information retention, entropy budget, magnitude scaling, and global information gates that generalizes standard VIB as a special case and characterize the role of the entropy budget as an early-training stabilizer that prevents variance explosion before convergence.
Learning with noisy labels (LNL) remains challenging, especially when the identification of clean samples relies heavily on the predictions of the model being trained. In such cases, early-stage selection errors may be reinforced during iterative optimization, leading to unreliable supervision. To alleviate this issue, a two-stage framework, termed CANNE, is proposed by combining Contrastive Language–Image Pre-training (CLIP)-based conservative offline cleaning with Adaptive Nearest Neighbors and eigenvector-based sample selection (ANNE)-based online refinement. Specifically, a high-confidence clean seed set is first constructed using two complementary probability sources derived from frozen CLIP representations and reliability criteria, including class-wise loss modeling and prediction consistency. This seed set is then used as a set of reliable anchors during the subsequent ANNE training process, where online feature- and neighborhood-based refinement further recovers and adjusts sample partitions. In this way, CANNE uses external vision–language priors to provide conservative and persistent guidance while preserving the adaptive recovery ability of online noisy-label learning. Experimental results on CIFAR-10, CIFAR-100, Animal-10N, and Mini-WebVision, together with additional evaluation under open-set noise, show that the proposed method achieves competitive performance across diverse noisy-label settings. In particular, CANNE achieves 96.6% and 96.3% best accuracies on CIFAR-10 under 80% and 90% symmetric noise, respectively, and 81.0% and 79.0% on CIFAR-100 under 20% and 50% symmetric noise. Additional repeated-run, threshold-sensitivity, and runtime analyses further indicate that the CLIP-based seed set provides stable guidance with only moderate computational overhead.
Ge Jin, Qian Zhang, Li Huang et al.· Entropy· 0 citations
This tutorial covers recent developments in OOD detection from both theoretical and practical perspectives, including four major categories: (1) post-hoc methods, (2) training-based methods with auxiliary outliers, (3) training methods without auxiliary outliers, and (4) foundation model-based approaches, along with recent advances in each area.
Suranga Seneviratne, Dishanika Denipitiyage, Sanjay Chawla et al.· Proceedings of the 32nd ACM...· 0 citations
An Embedded One-Class Classification (EOCC) framework for monitoring task-informed neural network representations andComparisons with depth-based, density-based, covariance-based, isolation-based, and end-to-end deep one-class methods show that EOCC is competitive and frequently achieves low Type II error while maintaining the nominal in-control acceptance probability.
Edgard M. Maboudou-Tchao, Poorna Sandamini Senaratne, Randyll Pandohie et al.· Mathematics· 0 citations