Feng, Chen;
Tzimiropoulos, Georgios;
Patras, Ioannis;
(2024)
CLIPCleaner: Cleaning Noisy Labels with CLIP.
In:
Proceedings of the 32nd ACM International Conference on Multimedia.
(pp. pp. 876-885).
ACM
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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 (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, 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.
| Type: | Proceedings paper |
|---|---|
| Title: | CLIPCleaner: Cleaning Noisy Labels with CLIP |
| Event: | MM '24: The 32nd ACM International Conference on Multimedia |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3664647.3680664 |
| Publisher version: | https://doi.org/10.1145/3664647.3680664 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Sample selection, Noisy Labels, CLIP |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10199672 |
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