15,107 research outputs found
Adversarial Discriminative Domain Adaptation
Adversarial learning methods are a promising approach to training robust deep
networks, and can generate complex samples across diverse domains. They also
can improve recognition despite the presence of domain shift or dataset bias:
several adversarial approaches to unsupervised domain adaptation have recently
been introduced, which reduce the difference between the training and test
domain distributions and thus improve generalization performance. Prior
generative approaches show compelling visualizations, but are not optimal on
discriminative tasks and can be limited to smaller shifts. Prior discriminative
approaches could handle larger domain shifts, but imposed tied weights on the
model and did not exploit a GAN-based loss. We first outline a novel
generalized framework for adversarial adaptation, which subsumes recent
state-of-the-art approaches as special cases, and we use this generalized view
to better relate the prior approaches. We propose a previously unexplored
instance of our general framework which combines discriminative modeling,
untied weight sharing, and a GAN loss, which we call Adversarial Discriminative
Domain Adaptation (ADDA). We show that ADDA is more effective yet considerably
simpler than competing domain-adversarial methods, and demonstrate the promise
of our approach by exceeding state-of-the-art unsupervised adaptation results
on standard cross-domain digit classification tasks and a new more difficult
cross-modality object classification task
Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos
Despite rapid advances in face recognition, there remains a clear gap between
the performance of still image-based face recognition and video-based face
recognition, due to the vast difference in visual quality between the domains
and the difficulty of curating diverse large-scale video datasets. This paper
addresses both of those challenges, through an image to video feature-level
domain adaptation approach, to learn discriminative video frame
representations. The framework utilizes large-scale unlabeled video data to
reduce the gap between different domains while transferring discriminative
knowledge from large-scale labeled still images. Given a face recognition
network that is pretrained in the image domain, the adaptation is achieved by
(i) distilling knowledge from the network to a video adaptation network through
feature matching, (ii) performing feature restoration through synthetic data
augmentation and (iii) learning a domain-invariant feature through a domain
adversarial discriminator. We further improve performance through a
discriminator-guided feature fusion that boosts high-quality frames while
eliminating those degraded by video domain-specific factors. Experiments on the
YouTube Faces and IJB-A datasets demonstrate that each module contributes to
our feature-level domain adaptation framework and substantially improves video
face recognition performance to achieve state-of-the-art accuracy. We
demonstrate qualitatively that the network learns to suppress diverse artifacts
in videos such as pose, illumination or occlusion without being explicitly
trained for them.Comment: accepted for publication at International Conference on Computer
Vision (ICCV) 201
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