Practical Insights of Repairing Model Problems on Image Classification. (arXiv:2205.07116v1 [cs.LG] CROSS LISTED)

Additional training of a deep learning model can cause negative effects on
the results, turning an initially positive sample into a negative one
(degradation). Such degradation is possible in real-world use cases due to the
diversity of sample characteristics. That is, a set of samples is a mixture of
critical ones which should not be missed and less important ones. Therefore, we
cannot understand the performance by accuracy alone. While existing research
aims to prevent a model degradation, insights into …

arxiv classification image insights