To deal with the domain shift between training and test samples, current
methods have primarily focused on learning generalizable features during
training and ignore the specificity of unseen samples that are also critical
during the test. In this paper, we investigate a more challenging task that
aims to adapt a trained CNN model to unseen domains during the test. To
maximumly mine the information in the test data, we propose a unified method
called DomainAdaptor for the test-time adaptation, which consists of an
AdaMixBN module and a Generalized Entropy Minimization (GEM) loss.
Specifically, AdaMixBN addresses the domain shift by adaptively fusing training
and test statistics in the normalization layer via a dynamic mixture
coefficient and a statistic transformation operation. To further enhance the
adaptation ability of AdaMixBN, we design a GEM loss that extends the Entropy
Minimization loss to better exploit the information in the test data. Extensive
experiments show that DomainAdaptor consistently outperforms the
state-of-the-art methods on four benchmarks. Furthermore, our method brings
more remarkable improvement against existing methods on the few-data unseen
domain. The code is available at this https URL

通过 AdaMixBN 模块和广义熵最小化 (GEM) 损失函数，本论文提出了一种名为 DomainAdaptor 的统一方法，用于进行测试时的适应学习，以最大限度地挖掘测试数据中的信息，并解决训练和测试样本之间的领域偏移问题，从而在四个基准测试中稳定超过现有方法，尤其在少量数据的未知领域中带来了更显著的改进。

域适应器：一种用于测试时适应的新方法

DomainAdaptor: A Novel Approach to Test-time Adaptation

Automatic speech recognition (ASR) models are frequently exposed to data
distribution shifts in many real-world scenarios, leading to erroneous
predictions. To tackle this issue, an existing test-time adaptation (TTA)
method has recently been proposed to adapt the pre-trained ASR model on
unlabeled test instances without source data. Despite decent performance gain,
this work relies solely on naive greedy decoding and performs adaptation across
timesteps at a frame level, which may not be optimal given the sequential
nature of the model output. Motivated by this, we propose a novel TTA
framework, dubbed SGEM, for general ASR models. To treat the sequential output,
SGEM first exploits beam search to explore candidate output logits and selects
the most plausible one. Then, it utilizes generalized entropy minimization and
negative sampling as unsupervised objectives to adapt the model. SGEM achieves
state-of-the-art performance for three mainstream ASR models under various
domain shifts.

该研究提出了一种新的测试时间适应（TTA）框架 SGEM，利用波束搜索和广义熵最小化以及负采样等无监督目标来适应模型，该框架在各种领域转移下取得了三种主流自动语音识别（ASR）模型的最先进性能。