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arXiv · 2011.12979

mask-Net: Learning Context Aware Invariant Features using Adversarial Forgetting (Student Abstract)

Hemant Yadav·Atul Anshuman Singh·Rachit Mittal·Sunayana Sitaram·Yi Yu·Rajiv Ratn Shah
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Abstract

Training a robust system, e.g.,Speech to Text (STT), requires large datasets. Variability present in the dataset such as unwanted nuisances and biases are the reason for the need of large datasets to learn general representations. In this work, we propose a novel approach to induce invariance using adversarial forgetting (AF). Our initial experiments on learning invariant features such as accent on the STT task achieve better generalizations in terms of word error rate (WER) compared to the traditional models. We observe an absolute improvement of 2.2% and 1.3% on out-of-distribution and in-distribution test sets, respectively.

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Hemant Yadav, Atul Anshuman Singh, Rachit Mittal, Sunayana Sitaram, Yi Yu, Rajiv Ratn Shah. 2020-11-25. mask-Net: Learning Context Aware Invariant Features using Adversarial Forgetting (Student Abstract). https://arxiv.org/abs/2011.12979

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