Imagenet C

ImageNet-C is a benchmark dataset used to evaluate the robustness of image classification models against various types of image corruptions, such as noise, blur, and compression artifacts. Current research focuses on improving model robustness through techniques like incorporating biologically-inspired mechanisms into convolutional neural networks (CNNs), developing novel attention mechanisms in vision transformers (ViTs), and employing adaptive learning rate strategies and data augmentation methods tailored to specific corruption types. This research is crucial for advancing the reliability and real-world applicability of computer vision systems, particularly in scenarios with noisy or degraded image data, impacting fields like medical imaging and robotics.