Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving image classifiers for small datasets by learning rate
  adaptations   Our paper introduces an efficient combination of established techniques to
improve classifier performance, in terms of accuracy and training time. We
achieve two-fold to ten-fold speedup in nearing state of the art accuracy, over
different model architectures, by dynamically tuning the learning rate. We find
it especially beneficial in the case of a small dataset, where reliability of
machine reasoning is lower. We validate our approach by comparing our method
versus vanilla training on CIFAR-10. We also demonstrate its practical
viability by implementing on an unbalanced corpus of diagnostic images.

30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int