Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LEEP: A New Measure to Evaluate Transferability of Learned
  Representations   We introduce a new measure to evaluate the transferability of representations
learned by classifiers. Our measure, the Log Expected Empirical Prediction
(LEEP), is simple and easy to compute: when given a classifier trained on a
source data set, it only requires running the target data set through this
classifier once. We analyze the properties of LEEP theoretically and
demonstrate its effectiveness empirically. Our analysis shows that LEEP can
predict the performance and convergence speed of both transfer and
meta-transfer learning methods, even for small or imbalanced data. Moreover,
LEEP outperforms recently proposed transferability measures such as negative
conditional entropy and H scores. Notably, when transferring from ImageNet to
CIFAR100, LEEP can achieve up to 30% improvement compared to the best competing
method in terms of the correlations with actual transfer accuracy.

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The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: chem-ph
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The categories of this paper are: q-alg
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The categories of this paper are: supr-con
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The categories of this paper are: solv-int
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-lat
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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph