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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
Learning Generative Models across Incomparable Spaces
Generative Adversarial Networks have shown remarkable success in learning a
distribution that faithfully recovers a reference distribution in its entirety.
However, in some cases, we may want to only learn some aspects (e.g., cluster
or manifold structure), while modifying others (e.g., style, orientation or
dimension). In this work, we propose an approach to learn generative models
across such incomparable spaces, and demonstrate how to steer the learned
distribution towards target properties. A key component of our model is the
Gromov-Wasserstein distance, a notion of discrepancy that compares
distributions relationally rather than absolutely. While this framework
subsumes current generative models in identically reproducing distributions,
its inherent flexibility allows application to tasks in manifold learning,
relational learning and cross-domain learning.Comment: International Conference on Machine Learning (ICML
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