We extend semi-supervised learning to the problem of domain adaptation to
learn significantly higher-accuracy models that train on one data distribution
and test on a different one. With the goal of generality, we introduce
AdaMatch, a method that unifies the tasks of unsupervised domain adaptation
(UDA), semi-supervised learning (SSL), and semi-supervised domain adaptation
(SSDA). In an extensive experimental study, we compare its behavior with
respective state-of-the-art techniques from SSL, SSDA, and UDA on vision
classification tasks. We find AdaMatch either matches or significantly exceeds
the state-of-the-art in each case using the same hyper-parameters regardless of
the dataset or task. For example, AdaMatch nearly doubles the accuracy compared
to that of the prior state-of-the-art on the UDA task for DomainNet and even
exceeds the accuracy of the prior state-of-the-art obtained with pre-training
by 6.4% when AdaMatch is trained completely from scratch. Furthermore, by
providing AdaMatch with just one labeled example per class from the target
domain (i.e., the SSDA setting), we increase the target accuracy by an
additional 6.1%, and with 5 labeled examples, by 13.6%.

介绍 AdaMatch 方法来统一无监督域自适应（UDA）、半监督学习（SSL）和半监督域自适应（SSDA）的任务，实现在一个数据分布上训练，在另一个数据分布上测试具有极高准确性的模型。实验结果表明，使用 AdaMatch 方法，无论是在哪个数据集或任务上，有标签还是无标签，其结果都能匹配或明显超过 SSL、SSDA 和 UDA 领域最先进的技术，而且仅使用相同的超参数即可。

AdaMatch：一种半监督学习和域适应的统一方法

AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain  Adaptation

We show that the influence of a subset of the training samples can be removed
-- or "forgotten" -- from the weights of a network trained on large-scale image
classification tasks, and we provide strong computable bounds on the amount of
remaining information after forgetting. Inspired by real-world applications of
forgetting techniques, we introduce a novel notion of forgetting in
mixed-privacy setting, where we know that a "core" subset of the training
samples does not need to be forgotten. While this variation of the problem is
conceptually simple, we show that working in this setting significantly
improves the accuracy and guarantees of forgetting methods applied to vision
classification tasks. Moreover, our method allows efficient removal of all
information contained in non-core data by simply setting to zero a subset of
the weights with minimal loss in performance. We achieve these results by
replacing a standard deep network with a suitable linear approximation. With
opportune changes to the network architecture and training procedure, we show
that such linear approximation achieves comparable performance to the original
network and that the forgetting problem becomes quadratic and can be solved
efficiently even for large models. Unlike previous forgetting methods on deep
networks, ours can achieve close to the state-of-the-art accuracy on large
scale vision tasks. In particular, we show that our method allows forgetting
without having to trade off the model accuracy.

本篇文章提出了一种遗忘技术，可以在大规模视觉分类任务中从网络训练的权重中移除一部分训练样本的影响，同时在混合隐私设置中保留核心样本的信息，采用合适的线性逼近替换标准深度网络可以实现相当的精度且达到最前沿的效果。