Addressing the large distribution gap between training and testing data has
long been a challenge in machine learning, giving rise to fields such as
transfer learning and domain adaptation. Recently, Continuous Domain Adaptation
(CDA) has emerged as an effective technique, closing this gap by utilizing a
series of intermediate domains. This paper contributes a novel CDA method,
W-MPOT, which rigorously addresses the domain ordering and error accumulation
problems overlooked by previous studies. Specifically, we construct a transfer
curriculum over the source and intermediate domains based on Wasserstein
distance, motivated by theoretical analysis of CDA. Then we transfer the source
model to the target domain through multiple valid paths in the curriculum using
a modified version of continuous optimal transport. A bidirectional path
consistency constraint is introduced to mitigate the impact of accumulated
mapping errors during continuous transfer. We extensively evaluate W-MPOT on
multiple datasets, achieving up to 54.1\% accuracy improvement on multi-session
Alzheimer MR image classification and 94.7\% MSE reduction on battery capacity
estimation.

该论文介绍了一种新颖的连续域适应方法 W-MPOT，通过构建基于 Wasserstein 距离的源域和中间域的转移课程表，使用连续最优传送的修改版本在转移课程表中的多个有效路径上将源模型转移到目标域，引入双向路径一致性约束以减轻连续转移过程中累积映射误差的影响，在多个数据集上广泛评估 W-MPOT，在多次会话的阿尔茨海默 MR 图像分类中提高了 54.1％的准确性，并在电池容量估计中降低了 94.7％的 MSE。

增强多路径传递课程的连续领域适应

Enhancing Continuous Domain Adaptation with Multi-Path Transfer  Curriculum

Existing domain adaptation methods assume that domain discrepancies are
caused by a few discrete attributes and variations, e.g., art, real, painting,
quickdraw, etc. We argue that this is not realistic as it is implausible to
define the real-world datasets using a few discrete attributes. Therefore, we
propose to investigate a new problem namely the Continuous Domain Adaptation
(CDA) through the lens where infinite domains are formed by continuously
varying attributes. Leveraging knowledge of two labeled source domains and
several observed unlabeled target domains data, the objective of CDA is to
learn a generalized model for whole data distribution with the continuous
attribute. Besides the contributions of formulating a new problem, we also
propose a novel approach as a strong CDA baseline. To be specific, firstly we
propose a novel alternating training strategy to reduce discrepancies among
multiple domains meanwhile generalize to unseen target domains. Secondly, we
propose a continuity constraint when estimating the cross-domain divergence
measurement. Finally, to decouple the discrepancy from the mini-batch size, we
design a domain-specific queue to maintain the global view of the source domain
that further boosts the adaptation performances. Our method is proven to
achieve the state-of-the-art in CDA problem using extensive experiments. The
code is available at this https URL

本文提出了连续域自适应（CDA）问题，并提出了一种新颖的方法作为 CDA 基线。该方法通过交替训练策略、连续性约束和域特定队列等手段，在多个域之间降低差异性并泛化到未见目标域，从而在 CDA 问题中实现了最优性能。