Jo-SRC: A Contrastive Approach for Combating Noisy Labels

• URL: http://arxiv.org/abs/2103.13029v1
• Date: Wed, 24 Mar 2021 07:26:07 GMT
• Title: Jo-SRC: A Contrastive Approach for Combating Noisy Labels
• Authors: Yazhou Yao, Zeren Sun, Chuanyi Zhang, Fumin Shen, Qi Wu, Jian Zhang, and Zhenmin Tang
• Abstract summary: We propose a noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency) Specifically, we train the network in a contrastive learning manner. Predictions from two different views of each sample are used to estimate its "likelihood" of being clean or out-of-distribution.
• Score: 58.867237220886885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance. Existing state-of-the-art methods primarily adopt a sample selection strategy, which selects small-loss samples for subsequent training. However, prior literature tends to perform sample selection within each mini-batch, neglecting the imbalance of noise ratios in different mini-batches. Moreover, valuable knowledge within high-loss samples is wasted. To this end, we propose a noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency). Specifically, we train the network in a contrastive learning manner. Predictions from two different views of each sample are used to estimate its "likelihood" of being clean or out-of-distribution. Furthermore, we propose a joint loss to advance the model generalization performance by introducing consistency regularization. Extensive experiments have validated the superiority of our approach over existing state-of-the-art methods.

Related papers

• Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training [20.492630610281658]
  Diffusion models learn to denoise data and the trained denoiser is then used to generate new samples from the data distribution. We introduce a new self-supervised training objective that differentiates the levels of noise added to a sample. We show by diverse experiments that the proposed contrastive diffusion training is effective for both sequential and parallel settings.
  arXiv  Detail & Related papers  (2024-07-12T03:03:50Z)
• Foster Adaptivity and Balance in Learning with Noisy Labels [26.309508654960354]
  We propose a novel approach named textbfSED to deal with label noise in a textbfSelf-adaptivtextbfE and class-balancetextbfD manner. A mean-teacher model is then employed to correct labels of noisy samples. We additionally propose a self-adaptive and class-balanced sample re-weighting mechanism to assign different weights to detected noisy samples.
  arXiv  Detail & Related papers  (2024-07-03T03:10:24Z)
• Debiased Sample Selection for Combating Noisy Labels [24.296451733127956]
  We propose a noIse-Tolerant Expert Model (ITEM) for debiased learning in sample selection. Specifically, to mitigate the training bias, we design a robust network architecture that integrates with multiple experts. By training on the mixture of two class-discriminative mini-batches, the model mitigates the effect of the imbalanced training set.
  arXiv  Detail & Related papers  (2024-01-24T10:37:28Z)
• Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning [76.00798972439004]
  Collaborative Sample Selection (CSS) removes noisy samples from identified clean set. We introduce a co-training mechanism with a contrastive loss in semi-supervised learning.
  arXiv  Detail & Related papers  (2023-10-24T05:37:20Z)
• Combating Label Noise With A General Surrogate Model For Sample Selection [84.61367781175984]
  We propose to leverage the vision-language surrogate model CLIP to filter noisy samples automatically. We validate the effectiveness of our proposed method on both real-world and synthetic noisy datasets.
  arXiv  Detail & Related papers  (2023-10-16T14:43:27Z)
• PASS: Peer-Agreement based Sample Selection for training with Noisy Labels [16.283722126438125]
  The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects. Current methodologies often rely on the small-loss hypothesis or feature-based selection to separate noisy- and clean-label samples. We propose a new noisy-label detection method, termed Peer-Agreement based Sample Selection (PASS), to address this problem.
  arXiv  Detail & Related papers  (2023-03-20T00:35:33Z)
• Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning [54.61762276179205]
  We propose a novel contrastive learning approach, MMBS, for building robust VQA models by Making the Most of Biased Samples. Specifically, we construct positive samples for contrastive learning by eliminating the information related to spurious correlation from the original training samples. We validate our contributions by achieving competitive performance on the OOD dataset VQA-CP v2 while preserving robust performance on the ID dataset VQA v2.
  arXiv  Detail & Related papers  (2022-10-10T11:05:21Z)
• Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels [87.48541631675889]
  We propose a two-stage clean samples identification method. First, we employ a class-level feature clustering procedure for the early identification of clean samples. Second, for the remaining clean samples that are close to the ground truth class boundary, we propose a novel consistency-based classification method.
  arXiv  Detail & Related papers  (2022-07-29T04:54:57Z)
• 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)
• Unified Regularity Measures for Sample-wise Learning and Generalization [18.10522585996242]
  We propose a pair of sample regularity measures for both processes with a formulation-consistent representation. Experiments validated the effectiveness and robustness of the proposed approaches for mini-batch SGD optimization.
  arXiv  Detail & Related papers  (2021-08-09T10:11:14Z)

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.