Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks
This work addresses the problem of adapting models across domains without labeled target data, which is crucial for real-world applications like autonomous driving, but it appears incremental as it builds on existing deep learning methods.
The paper tackles unsupervised domain adaptation in computer vision by introducing a cross-grafted representation stacking mechanism with variational auto-encoders and generative adversarial networks for label alignment, achieving state-of-the-art performance on multiple benchmarks.
Current deep domain adaptation methods used in computer vision have mainly focused on learning discriminative and domain-invariant features across different domains. In this paper, we present a novel approach that bridges the domain gap by projecting the source and target domains into a common association space through an unsupervised ``cross-grafted representation stacking'' (CGRS) mechanism. Specifically, we construct variational auto-encoders (VAE) for the two domains, and form bidirectional associations by cross-grafting the VAEs' decoder stacks. Furthermore, generative adversarial networks (GAN) are employed for label alignment (LA), mapping the target domain data to the known label space of the source domain. The overall adaptation process hence consists of three phases: feature representation learning by VAEs, association generation, and association label alignment by GANs. Experimental results demonstrate that our CGRS-LA approach outperforms the state-of-the-art on a number of unsupervised domain adaptation benchmarks.