4 research outputs found
DACS: Domain Adaptation via Cross-domain Mixed Sampling
Semantic segmentation models based on convolutional neural networks have
recently displayed remarkable performance for a multitude of applications.
However, these models typically do not generalize well when applied on new
domains, especially when going from synthetic to real data. In this paper we
address the problem of unsupervised domain adaptation (UDA), which attempts to
train on labelled data from one domain (source domain), and simultaneously
learn from unlabelled data in the domain of interest (target domain). Existing
methods have seen success by training on pseudo-labels for these unlabelled
images. Multiple techniques have been proposed to mitigate low-quality
pseudo-labels arising from the domain shift, with varying degrees of success.
We propose DACS: Domain Adaptation via Cross-domain mixed Sampling, which mixes
images from the two domains along with the corresponding labels and
pseudo-labels. These mixed samples are then trained on, in addition to the
labelled data itself. We demonstrate the effectiveness of our solution by
achieving state-of-the-art results for GTA5 to Cityscapes, a common
synthetic-to-real semantic segmentation benchmark for UDA.Comment: This paper has been accepted to WACV202
Deep Adversarial Transition Learning using Cross-Grafted Generative Stacks
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 "deep adversarial
transition learning" (DATL) framework that bridges the domain gap by projecting
the source and target domains into intermediate, transitional spaces through
the employment of adjustable, cross-grafted generative network stacks and
effective adversarial learning between transitions. Specifically, we construct
variational auto-encoders (VAE) for the two domains, and form bidirectional
transitions by cross-grafting the VAEs' decoder stacks. Furthermore, generative
adversarial networks (GAN) are employed for domain adaptation, 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, transitions generation, and transitions alignment by GANs.
Experimental results demonstrate that our method outperforms the state-of-the
art on a number of unsupervised domain adaptation benchmarks.Comment: 12 pages, 8 figure