  In a recent decade, ImageNet has become the most notable and powerful
benchmark database in computer vision and machine learning community. As
ImageNet has emerged as a representative benchmark for evaluating the
performance of novel deep learning models, its evaluation tends to include only
quantitative measures such as error rate, rather than qualitative analysis.
Thus, there are few studies that analyze the failure cases of deep learning
models in ImageNet, though there are numerous works analyzing the networks
themselves and visualizing them. In this abstract, we qualitatively analyze the
failure cases of ImageNet classification results from recent deep learning
model, and categorize these cases according to the certain image patterns.
Through this failure analysis, we believe that it can be discovered what the
final challenges are in ImageNet database, which the current deep learning
model is still vulnerable to.
