Dual-Representation-Based Autoencoder for Domain Adaptation
提出一种双表示自编码器（DRAE），通过同时学习全局和局部表示来保留类别判别信息，解决域适应中分布对齐导致分类性能下降的问题，在文本和图像数据集上验证了有效性。
Domain adaptation aims to facilitate the learning task in an unlabeled target domain by leveraging the auxiliary knowledge in a well-labeled source domain from a different distribution. Almost existing autoencoder-based domain adaptation approaches focus on learning domain-invariant representations to reduce the distribution discrepancy between source and target domains. However, there is still a weakness existing in these approaches: the class-discriminative information of the two domains may be damaged while aligning the distributions of the source and target domains, which makes the samples with different classes close to each other, leading to performance degradation. To tackle this issue, we propose a novel dual-representation autoencoder (DRAE) to learn dual-domain-invariant representations for domain adaptation. Specifically, DRAE consists of three learning phases. First, DRAE learns global representations of all source and target data to maximize the interclass distance in each domain and minimize the marginal distribution and conditional distribution of both domains simultaneously. Second, DRAE extracts local representations of instances sharing the same label in both domains to maintain class-discriminative information in each class. Finally, DRAE constructs dual representations by aligning the global and local representations with different weights. Using three text and two image datasets and 12 state-of-the-art domain adaptation methods, the extensive experiments have demonstrated the effectiveness of DRAE.