CLIPCleaner: Cleaning Noisy Labels with CLIP

• URL: http://arxiv.org/abs/2408.10012v2
• Date: Mon, 16 Sep 2024 11:46:36 GMT
• Title: CLIPCleaner: Cleaning Noisy Labels with CLIP
• Authors: Chen Feng, Georgios Tzimiropoulos, Ioannis Patras,
• Abstract summary: textitCLIPCleaner is a zero-shot classifier for efficient, offline, clean sample selection. textitCLIPCleaner offers a simple, single-step approach that achieves competitive or superior performance on benchmark datasets.
• Score: 36.434849361479316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning with Noisy labels (LNL) poses a significant challenge for the Machine Learning community. Some of the most widely used approaches that select as clean samples for which the model itself (the in-training model) has high confidence, e.g., `small loss', can suffer from the so called `self-confirmation' bias. This bias arises because the in-training model, is at least partially trained on the noisy labels. Furthermore, in the classification case, an additional challenge arises because some of the label noise is between classes that are visually very similar (`hard noise'). This paper addresses these challenges by proposing a method (\textit{CLIPCleaner}) that leverages CLIP, a powerful Vision-Language (VL) model for constructing a zero-shot classifier for efficient, offline, clean sample selection. This has the advantage that the sample selection is decoupled from the in-training model and that the sample selection is aware of the semantic and visual similarities between the classes due to the way that CLIP is trained. We provide theoretical justifications and empirical evidence to demonstrate the advantages of CLIP for LNL compared to conventional pre-trained models. Compared to current methods that combine iterative sample selection with various techniques, \textit{CLIPCleaner} offers a simple, single-step approach that achieves competitive or superior performance on benchmark datasets. To the best of our knowledge, this is the first time a VL model has been used for sample selection to address the problem of Learning with Noisy Labels (LNL), highlighting their potential in the domain.

Related papers

• 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)
• 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)
• RanPAC: Random Projections and Pre-trained Models for Continual Learning [59.07316955610658]
  Continual learning (CL) aims to learn different tasks (such as classification) in a non-stationary data stream without forgetting old ones. We propose a concise and effective approach for CL with pre-trained models.
  arXiv  Detail & Related papers  (2023-07-05T12:49:02Z)
• 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)
• Class Prototype-based Cleaner for Label Noise Learning [73.007001454085]
  Semi-supervised learning methods are current SOTA solutions to the noisy-label learning problem. We propose a simple yet effective solution, named textbfClass textbfPrototype-based label noise textbfCleaner.
  arXiv  Detail & Related papers  (2022-12-21T04:56:41Z)
• NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning [8.749830466953584]
  Semi-Supervised Learning (SSL) aims to learn a model using a tiny labeled set and massive amounts of unlabeled data. In this work we introduce a new framework for SSL named NorMatch. We demonstrate, through numerical and visual results, that NorMatch achieves state-of-the-art performance on several datasets.
  arXiv  Detail & Related papers  (2022-11-17T15:39:18Z)
• 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)
• L2B: Learning to Bootstrap Robust Models for Combating Label Noise [52.02335367411447]
  This paper introduces a simple and effective method, named Learning to Bootstrap (L2B) It enables models to bootstrap themselves using their own predictions without being adversely affected by erroneous pseudo-labels. It achieves this by dynamically adjusting the importance weight between real observed and generated labels, as well as between different samples through meta-learning.
  arXiv  Detail & Related papers  (2022-02-09T05:57:08Z)

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.