Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective

• URL: http://arxiv.org/abs/2111.03874v1
• Date: Sat, 6 Nov 2021 12:53:34 GMT
• Title: Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective
• Authors: Zhengzhuo Xu, Zenghao Chai, Chun Yuan
• Abstract summary: Real-world data universally confronts a severe class-imbalance problem and exhibits a long-tailed distribution. We propose two novel methods from the prior perspective to alleviate this dilemma. First, we deduce a balance-oriented data augmentation named Uniform Mixup (UniMix) to promote mixup in long-tailed scenarios. Second, motivated by the Bayesian theory, we figure out the Bayes Bias (Bayias) to compensate it as a modification on standard cross-entropy loss.
• Score: 17.733087434470907
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-world data universally confronts a severe class-imbalance problem and exhibits a long-tailed distribution, i.e., most labels are associated with limited instances. The na\"ive models supervised by such datasets would prefer dominant labels, encounter a serious generalization challenge and become poorly calibrated. We propose two novel methods from the prior perspective to alleviate this dilemma. First, we deduce a balance-oriented data augmentation named Uniform Mixup (UniMix) to promote mixup in long-tailed scenarios, which adopts advanced mixing factor and sampler in favor of the minority. Second, motivated by the Bayesian theory, we figure out the Bayes Bias (Bayias), an inherent bias caused by the inconsistency of prior, and compensate it as a modification on standard cross-entropy loss. We further prove that both the proposed methods ensure the classification calibration theoretically and empirically. Extensive experiments verify that our strategies contribute to a better-calibrated model, and their combination achieves state-of-the-art performance on CIFAR-LT, ImageNet-LT, and iNaturalist 2018.

Related papers

• Marginal Debiased Network for Fair Visual Recognition [65.64172835624206]
  We propose a novel marginal debiased network (MDN) to learn debiased representations. More specifically, a marginal softmax loss (MSL) is designed by introducing the idea of margin penalty into the fairness problem. Our MDN can achieve a remarkable performance on under-represented samples and obtain superior debiased results against the previous approaches.
  arXiv  Detail & Related papers  (2024-01-04T08:57:09Z)
• Twice Class Bias Correction for Imbalanced Semi-Supervised Learning [59.90429949214134]
  We introduce a novel approach called textbfTwice textbfClass textbfBias textbfCorrection (textbfTCBC) We estimate the class bias of the model parameters during the training process. We apply a secondary correction to the model's pseudo-labels for unlabeled samples.
  arXiv  Detail & Related papers  (2023-12-27T15:06:36Z)
• Fairness under Covariate Shift: Improving Fairness-Accuracy tradeoff with few Unlabeled Test Samples [21.144077993862652]
  We operate in the unsupervised regime where only a small set of unlabeled test samples along with a labeled training set is available. We experimentally verify that optimizing with our loss formulation outperforms a number of state-of-the-art baselines. We show that our proposed method significantly outperforms them.
  arXiv  Detail & Related papers  (2023-10-11T14:39:51Z)
• Fair GANs through model rebalancing for extremely imbalanced class distributions [5.463417677777276]
  We present an approach to construct an unbiased generative adversarial network (GAN) from an existing biased GAN. We show results for the StyleGAN2 models while training on the Flickr Faces High Quality (FFHQ) dataset for racial fairness. We further validate our approach by applying it to an imbalanced CIFAR10 dataset which is also twice as large.
  arXiv  Detail & Related papers  (2023-08-16T19:20:06Z)
• The Decaying Missing-at-Random Framework: Doubly Robust Causal Inference with Partially Labeled Data [10.021381302215062]
  In real-world scenarios, data collection limitations often result in partially labeled datasets, leading to difficulties in drawing reliable causal inferences. Traditional approaches in the semi-parametric (SS) and missing data literature may not adequately handle these complexities, leading to biased estimates. This framework tackles missing outcomes in high-dimensional settings and accounts for selection bias.
  arXiv  Detail & Related papers  (2023-05-22T07:37:12Z)
• Delving into Identify-Emphasize Paradigm for Combating Unknown Bias [52.76758938921129]
  We propose an effective bias-conflicting scoring method (ECS) to boost the identification accuracy. We also propose gradient alignment (GA) to balance the contributions of the mined bias-aligned and bias-conflicting samples. Experiments are conducted on multiple datasets in various settings, demonstrating that the proposed solution can mitigate the impact of unknown biases.
  arXiv  Detail & Related papers  (2023-02-22T14:50:24Z)
• On how to avoid exacerbating spurious correlations when models are overparameterized [33.315813572333745]
  We show that VS-loss learns a model that is fair towards minorities even when spurious features are strong. Compared to previous works, our bounds hold for more general models, they are non-asymptotic, and, they apply even at scenarios of extreme imbalance.
  arXiv  Detail & Related papers  (2022-06-25T21:53:44Z)
• Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing [104.630875328668]
  Mixup scheme suggests mixing a pair of samples to create an augmented training sample. We present a novel, yet simple Mixup-variant that captures the best of both worlds.
  arXiv  Detail & Related papers  (2021-12-16T11:27:48Z)
• Squared $\ell_2$ Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations [76.85274970052762]
  Regularizing distance between embeddings/representations of original samples and augmented counterparts is a popular technique for improving robustness of neural networks. In this paper, we explore these various regularization choices, seeking to provide a general understanding of how we should regularize the embeddings. We show that the generic approach we identified (squared $ell$ regularized augmentation) outperforms several recent methods, which are each specially designed for one task.
  arXiv  Detail & Related papers  (2020-11-25T22:40:09Z)
• Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer [72.5190560787569]
  In computer vision, learning from long tailed datasets is a recurring theme, especially for natural image datasets. Our proposal posits a meta-distributional scenario, where the data generating mechanism is invariant across the label-conditional feature distributions. This allows us to leverage a causal data inflation procedure to enlarge the representation of minority classes.
  arXiv  Detail & Related papers  (2020-11-25T00:13:11Z)

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.