Sep 19, 2023 · This paper examines the robustness of a multi-modal computer vision model, CLIP (Contrastive Language-Image Pretraining), in the context of unsupervised ...
The LP-CLIP technique offers a promising approach to enhance the robustness of CLIP without the need for annotations, and aims to improve the model's ...
Sep 19, 2023 · The LP-CLIP technique offers a promising approach to enhance the robust- ness of CLIP without the need for annotations. By leveraging a simple ...
Jun 27, 2023 · In this study, however, we show that knowledge distillation between large models can also be used to purely enhance adversarial robustness.
Knowledge distillation [21] is a widely used technique for transferring information from one model to another, typ- ically for the purpose of model compression.
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To address this challenge, we introduce a novel training framework based on cross-modal contrastive learning that uses progressive self-distillation and soft ...
Sep 7, 2024 · 2023. Improving clip robustness with knowledge distillation and self-training. arXiv preprint arXiv:2309.10361.
In Figure 3, we show qualitative examples of the instances where our method shows improved robustness over its. CLIP counterpart for out-of-distribution images ...
We also propose using KDIGA in an iterative self- distillation (ISD) training scheme, which can achieve better standard accuracy and adversarial robustness than ...
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This work develops a framework to investigate the key issues relate the most to training robust and lightweight models.