Class Confidence Aware Reweighting for Long Tailed Learning

• URL: http://arxiv.org/abs/2601.15924v1
• Date: Thu, 22 Jan 2026 12:58:05 GMT
• Title: Class Confidence Aware Reweighting for Long Tailed Learning
• Authors: Brainard Philemon Jagati, Jitendra Tembhurne, Harsh Goud, Rudra Pratap Singh, Chandrashekhar Meshram,
• Abstract summary: We present the design of a class and confidence-aware re-weighting scheme for long-tailed learning.<n>We use an (p_t, f_c) function to enable the modulation of the contribution towards the training task based upon the confidence value of the prediction.
• Score: 0.8297806372438926
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep neural network models degrade significantly in the long-tailed data distribution, with the overall training data dominated by a small set of classes in the head, and the tail classes obtaining less training examples. Addressing the imbalance in the classes, attention in the related literature was given mainly to the adjustments carried out in the decision space in terms of either corrections performed at the logit level in order to compensate class-prior bias, with the least attention to the optimization process resulting from the adjustments introduced through the differences in the confidences among the samples. In the current study, we present the design of a class and confidence-aware re-weighting scheme for long-tailed learning. This scheme is purely based upon the loss level and has a complementary nature to the existing methods performing the adjustment of the logits. In the practical implementation stage of the proposed scheme, we use an Ω(p_t, f_c) function. This function enables the modulation of the contribution towards the training task based upon the confidence value of the prediction, as well as the relative frequency of the corresponding class. Our observations in the experiments are corroborated by significant experimental results performed on the CIFAR-100-LT, ImageNet-LT, and iNaturalist2018 datasets under various values of imbalance factors that clearly authenticate the theoretical discussions above.

Related papers

• Online Bayesian Imbalanced Learning with Bregman-Calibrated Deep Networks [0.7106986689736825]
  We present textitOnline Bayesian Imbalanced Learning (OBIL), a principled framework that decouples likelihood-ratio estimation from class-prior assumptions.<n>Our approach builds on the established connection between Bregman divergences and proper scoring rules to show that deep networks trained with such losses produce posterior probability estimates.<n>We prove that these likelihood-ratio estimates remain valid under arbitrary changes in class priors and cost structures, requiring only a threshold adjustment for optimal Bayes decisions.
  arXiv  Detail & Related papers  (2026-02-08T21:23:00Z)
• Gradient Reweighting: Towards Imbalanced Class-Incremental Learning [8.438092346233054]
  Class-Incremental Learning (CIL) trains a model to continually recognize new classes from non-stationary data. A major challenge of CIL arises when applying to real-world data characterized by non-uniform distribution. We show that this dual imbalance issue causes skewed gradient updates with biased weights in FC layers, thus inducing over/under-fitting and catastrophic forgetting in CIL.
  arXiv  Detail & Related papers  (2024-02-28T18:08:03Z)
• Bias Mitigating Few-Shot Class-Incremental Learning [17.185744533050116]
  Few-shot class-incremental learning aims at recognizing novel classes continually with limited novel class samples. Recent methods somewhat alleviate the accuracy imbalance between base and incremental classes by fine-tuning the feature extractor in the incremental sessions. We propose a novel method to mitigate model bias of the FSCIL problem during training and inference processes.
  arXiv  Detail & Related papers  (2024-02-01T10:37:41Z)
• Simplifying Neural Network Training Under Class Imbalance [77.39968702907817]
  Real-world datasets are often highly class-imbalanced, which can adversely impact the performance of deep learning models. The majority of research on training neural networks under class imbalance has focused on specialized loss functions, sampling techniques, or two-stage training procedures. We demonstrate that simply tuning existing components of standard deep learning pipelines, such as the batch size, data augmentation, and label smoothing, can achieve state-of-the-art performance without any such specialized class imbalance methods.
  arXiv  Detail & Related papers  (2023-12-05T05:52:44Z)
• On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training [72.8087629914444]
  We study the impact of the trade-off between the intra-class diversity (the number of samples per class) and the inter-class diversity (the number of classes) of a supervised pre-training dataset. With the size of the pre-training dataset fixed, the best downstream performance comes with a balance on the intra-/inter-class diversity.
  arXiv  Detail & Related papers  (2023-05-20T16:23:50Z)
• Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning [60.41501515192088]
  Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively. The data samples usually follow a long-tailed distribution in the real world, and FL on the decentralized and long-tailed data yields a poorly-behaved global model. In this work, we integrate the local real data with the global gradient prototypes to form the local balanced datasets.
  arXiv  Detail & Related papers  (2023-01-25T03:18:10Z)
• Leveraging Angular Information Between Feature and Classifier for Long-tailed Learning: A Prediction Reformulation Approach [90.77858044524544]
  We reformulate the recognition probabilities through included angles without re-balancing the classifier weights. Inspired by the performance improvement of the predictive form reformulation, we explore the different properties of this angular prediction. Our method is able to obtain the best performance among peer methods without pretraining on CIFAR10/100-LT and ImageNet-LT.
  arXiv  Detail & Related papers  (2022-12-03T07:52:48Z)
• Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport [78.9167477093745]
  We propose a novel distribution calibration method by learning the adaptive weight matrix between novel samples and base classes. Experimental results on standard benchmarks demonstrate that our proposed plug-and-play model outperforms competing approaches.
  arXiv  Detail & Related papers  (2022-10-09T02:32:57Z)
• Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification [2.922007656878633]
  We propose a new clustering method based on Variational Bayesian inference, further improved by Adaptive Dimension Reduction. Our proposed method significantly improves accuracy in the realistic unbalanced transductive setting on various Few-Shot benchmarks.
  arXiv  Detail & Related papers  (2022-09-18T10:29:02Z)
• CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning [55.733193075728096]
  Modern deep neural networks can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate this data bias issue. We propose a meta-model capable of adaptively learning an explicit weighting scheme directly from data.
  arXiv  Detail & Related papers  (2022-02-11T13:49:51Z)
• Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation [19.259574003403998]
  In image segmentation neural networks may overfit to the foreground samples from small structures. In this study, we provide new insights on the problem of overfitting under class imbalance by inspecting the network behavior.
  arXiv  Detail & Related papers  (2021-02-20T14:57:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.