论文标题
面具网络：学习上下文意识到使用对抗性遗忘（学生摘要）的不变特征
mask-Net: Learning Context Aware Invariant Features using Adversarial Forgetting (Student Abstract)
论文作者
论文摘要
培训强大的系统，例如文本（STT）的语音需要大的数据集。数据集中存在的可变性（例如不必要的滋扰和偏见）是需要大型数据集学习一般表示形式的原因。在这项工作中，我们提出了一种新颖的方法来使用对抗性遗忘（AF）诱导不变性。与传统模型相比，我们关于学习不变特征（例如在单词错误率（WER）方面实现了更好的概括）的最初实验。我们观察到分别分布和分布测试集的绝对提高2.2％和1.3％。
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.
