Generalization is a fundamental problem in machine learning, involving training models to perform well on the data they were trained on, as well as unseen or modified data. A key subfield within generalization is model robustness, which ensures that machine learning models maintain their performance even when faced with changes in the input data. Imagine training a model on ImageNet and achieving impressive accuracy on that dataset. However, the model’s performance drops significantly when the test distribution changes, perhaps due to image corruptions like JPEG compression artifacts, adverse weather conditions like fog or rain, or image contrast and brightness alterations. Model robustness is critical in machine learning because real-world data is often dynamic and unpredictable. “A good model will be robust to those changes,” Mehmet tells us. “What we want to do is improve the robustness of our model to ensure it works in different distributions while keeping or improving its performance on the test set. How we do it is through data augmentation. 12 DAILY ICCV Wednesday Poster Presentation HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness Mehmet Kerim Yücel is a Research Scientist at Samsung Electronics Research and a recent PhD graduate from Hacettepe University in Ankara, Turkey. His paper proposes a new method leveraging data augmentation techniques to enhance the robustness of convolutional neural networks (CNNs). He speaks to us ahead of his poster this afternoon.
RkJQdWJsaXNoZXIy NTc3NzU=