Adaptive Sample Selection for Robust Learning under Label Noise
- URL: http://arxiv.org/abs/2106.15292v1
- Date: Tue, 29 Jun 2021 12:10:58 GMT
- Title: Adaptive Sample Selection for Robust Learning under Label Noise
- Authors: Deep Patel and P.S. Sastry
- Abstract summary: Deep Neural Networks (DNNs) have been shown to be susceptible to memorization or overfitting in the presence of noisily labelled data.
A prominent class of algorithms rely on sample selection strategies, motivated by curriculum learning.
We propose a data-dependent, adaptive sample selection strategy that relies only on batch statistics.
- Score: 1.71982924656402
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep Neural Networks (DNNs) have been shown to be susceptible to memorization
or overfitting in the presence of noisily labelled data. For the problem of
robust learning under such noisy data, several algorithms have been proposed. A
prominent class of algorithms rely on sample selection strategies, motivated by
curriculum learning. For example, many algorithms use the `small loss trick'
wherein a fraction of samples with loss values below a certain threshold are
selected for training. These algorithms are sensitive to such thresholds, and
it is difficult to fix or learn these thresholds. Often, these algorithms also
require information such as label noise rates which are typically unavailable
in practice. In this paper, we propose a data-dependent, adaptive sample
selection strategy that relies only on batch statistics of a given mini-batch
to provide robustness against label noise. The algorithm does not have any
additional hyperparameters for sample selection, does not need any information
on noise rates, and does not need access to separate data with clean labels. We
empirically demonstrate the effectiveness of our algorithm on benchmark
datasets.
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