798 research outputs found
From source to target and back: symmetric bi-directional adaptive GAN
The effectiveness of generative adversarial approaches in producing images
according to a specific style or visual domain has recently opened new
directions to solve the unsupervised domain adaptation problem. It has been
shown that source labeled images can be modified to mimic target samples making
it possible to train directly a classifier in the target domain, despite the
original lack of annotated data. Inverse mappings from the target to the source
domain have also been evaluated but only passing through adapted feature
spaces, thus without new image generation. In this paper we propose to better
exploit the potential of generative adversarial networks for adaptation by
introducing a novel symmetric mapping among domains. We jointly optimize
bi-directional image transformations combining them with target self-labeling.
Moreover we define a new class consistency loss that aligns the generators in
the two directions imposing to conserve the class identity of an image passing
through both domain mappings. A detailed qualitative and quantitative analysis
of the reconstructed images confirm the power of our approach. By integrating
the two domain specific classifiers obtained with our bi-directional network we
exceed previous state-of-the-art unsupervised adaptation results on four
different benchmark datasets
Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation
In this paper, we address the task of semantic-guided scene generation. One
open challenge in scene generation is the difficulty of the generation of small
objects and detailed local texture, which has been widely observed in global
image-level generation methods. To tackle this issue, in this work we consider
learning the scene generation in a local context, and correspondingly design a
local class-specific generative network with semantic maps as a guidance, which
separately constructs and learns sub-generators concentrating on the generation
of different classes, and is able to provide more scene details. To learn more
discriminative class-specific feature representations for the local generation,
a novel classification module is also proposed. To combine the advantage of
both the global image-level and the local class-specific generation, a joint
generation network is designed with an attention fusion module and a
dual-discriminator structure embedded. Extensive experiments on two scene image
generation tasks show superior generation performance of the proposed model.
The state-of-the-art results are established by large margins on both tasks and
on challenging public benchmarks. The source code and trained models are
available at https://github.com/Ha0Tang/LGGAN.Comment: Accepted to CVPR 2020, camera ready (10 pages) + supplementary (18
pages
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