We augment the images in a way that improves the accuracy by diversifying the training distribution. What we propose is a two-step algorithm. Firstly, the HybridAugment data augmentation method. Then we build on it and propose HybridAugment++, which is the end result of our paper.” Inspired by Mehmet’s background in electronics engineering, the paper delves into frequency analysis, a specific area of the robustness field, which plays a crucial role in signal processing. The central idea is that CNNs and humans process information differently. While CNNs focus on high-frequency components, human perception emphasizes lowfrequency information. This divergence in processing methods is one of the reasons adversarial examples, where small, imperceptible changes can fool a model, are a concern. Humans may not be able to perceive them visually, but CNNs do. “If I were to show you an image of a cat with a couple of pixels flipped, just some random noise, you will still classify it as a cat because we are robust to those pixel changes,” Mehmet points out. “We wanted to do the same thing on CNNs but with a frequency perspective!” 13 DAILY ICCV Wednesday Mehmet Kerim Yücel
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