Web: http://arxiv.org/abs/2206.07290

June 16, 2022, 1:13 a.m. | Felix Petersen, Hilde Kuehne, Christian Borgelt, Oliver Deussen

cs.CV updates on arXiv.org arxiv.org

The top-k classification accuracy is one of the core metrics in machine
learning. Here, k is conventionally a positive integer, such as 1 or 5, leading
to top-1 or top-5 training objectives. In this work, we relax this assumption
and optimize the model for multiple k simultaneously instead of using a single
k. Leveraging recent advances in differentiable sorting and ranking, we propose
a differentiable top-k cross-entropy classification loss. This allows training
the network while not only considering the top-1 …

arxiv classification learning lg top

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