Test-time adaptation (TTA) aims to adapt a trained classifier using online
unlabeled test data only, without any information related to the training
procedure. Most existing TTA methods adapt the trained classifier using the
classifier's prediction on the test data as pseudo-label. However, under
test-time domain shift, accuracy of the pseudo labels cannot be guaranteed, and
thus the TTA methods often encounter performance degradation at the adapted
classifier. To overcome this limitation, we propose a novel test-time
adaptation method, called Test-time Adaptation via Self-Training with nearest
neighbor information (TAST), which is composed of the following procedures: (1)
adds trainable adaptation modules on top of the trained feature extractor; (2)
newly defines a pseudo-label distribution for the test data by using the
nearest neighbor information; (3) trains these modules only a few times during
test time to match the nearest neighbor-based pseudo label distribution and a
prototype-based class distribution for the test data; and (4) predicts the
label of test data using the average predicted class distribution from these
modules. The pseudo-label generation is based on the basic intuition that a
test data and its nearest neighbor in the embedding space are likely to share
the same label under the domain shift. By utilizing multiple randomly
initialized adaptation modules, TAST extracts useful information for the
classification of the test data under the domain shift, using the nearest
neighbor information. TAST showed better performance than the state-of-the-art
TTA methods on two standard benchmark tasks, domain generalization, namely
VLCS, PACS, OfficeHome, and TerraIncognita, and image corruption, particularly
CIFAR-10/100C.

提出了一种新的测试时间自适应方法 Test-time Adaptation via Self-Training with nearest neighbor information (TAST)，其利用最近邻信息生成伪标签并通过自训练进行分类，能够在领域一般化和图像破坏等方面取得优于现有方法的性能。

借助最近邻信息的自我训练实现测试时间适应

Test-Time Adaptation via Self-Training with Nearest Neighbor Information

We introduce second-order vector representations of words, induced from
nearest neighborhood topological features in pre-trained contextual word
embeddings. We then analyze the effects of using second-order embeddings as
input features in two deep natural language processing models, for named entity
recognition and recognizing textual entailment, as well as a linear model for
paraphrase recognition. Surprisingly, we find that nearest neighbor information
alone is sufficient to capture most of the performance benefits derived from
using pre-trained word embeddings. Furthermore, second-order embeddings are
able to handle highly heterogeneous data better than first-order
representations, though at the cost of some specificity. Additionally,
augmenting contextual embeddings with second-order information further improves
model performance in some cases. Due to variance in the random initializations
of word embeddings, utilizing nearest neighbor features from multiple
first-order embedding samples can also contribute to downstream performance
gains. Finally, we identify intriguing characteristics of second-order
embedding spaces for further research, including much higher density and
different semantic interpretations of cosine similarity.

使用预训练的上下文词嵌入中的最近邻拓扑特征，我们介绍了二阶向量词表示法，并研究其在两种自然语言处理模型以及一个线性模型中的性能。具有更高的密度和不同的余弦相似性语义解释，二阶词嵌入为处理异质数据提供了更好的解决方案，此外，它们可以进一步提高模型性能。