What can we learn from misclassified ImageNet images?. (arXiv:2201.08098v1 [cs.CV])

Understanding the patterns of misclassified ImageNet images is particularly
important, as it could guide us to design deep neural networks (DNN) that
generalize better. However, the richness of ImageNet imposes difficulties for
researchers to visually find any useful patterns of misclassification. Here, to
help find these patterns, we propose "Superclassing ImageNet dataset". It is a
subset of ImageNet which consists of 10 superclasses, each containing 7-116
related subclasses (e.g., 52 bird types, 116 dog types). By training neural
networks on …

arxiv cv imagenet images