Overfitting, where a model performs exceptionally well on the training data but poorly on new, unseen data, is a significant concern in machine learning. Model robustness is closely connected to overfitting because robust models are less susceptible to changes in the data distribution and environment, which is a critical requirement if they are to be deployed in the real world. The work aims to contribute to the ongoing efforts to enhance model robustness in the context of computer vision. Still, Mehmet believes the proposed methods could benefit various downstream tasks, extending to other domains likenatural language processing. “I think our method would work off the shelf for many computer vision tasks,” he says. “Our work is primarily focused on vision and CNNs, but we show it also works for transformers. For NLP or other modalities, I don’t think it would be that easy. In NLP, the frequency of the words rather than the image is not trivial for me to define at this point. That’s why we’ve shared all the details in our paper, and all the code and all the pre-trained models are available on GitHub.” To learn more about Mehmet’s work, visit his poster this afternoon at 14:30-16:30. 15 DAILY ICCV Wednesday Mehmet Kerim Yücel
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