Open AccessPosted Content
Conditional Adversarial Domain Adaptation
TLDR
Conditional adversarial domain adaptation is presented, a principled framework that conditions the adversarial adaptation models on discriminative information conveyed in the classifier predictions to guarantee the transferability.Abstract:
Adversarial learning has been embedded into deep networks to learn disentangled and transferable representations for domain adaptation. Existing adversarial domain adaptation methods may not effectively align different domains of multimodal distributions native in classification problems. In this paper, we present conditional adversarial domain adaptation, a principled framework that conditions the adversarial adaptation models on discriminative information conveyed in the classifier predictions. Conditional domain adversarial networks (CDANs) are designed with two novel conditioning strategies: multilinear conditioning that captures the cross-covariance between feature representations and classifier predictions to improve the discriminability, and entropy conditioning that controls the uncertainty of classifier predictions to guarantee the transferability. With theoretical guarantees and a few lines of codes, the approach has exceeded state-of-the-art results on five datasets.read more
Citations
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A Comprehensive Survey on Transfer Learning
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A Survey on Deep Transfer Learning
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Strong-Weak Distribution Alignment for Adaptive Object Detection
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Out-of-Distribution Generalization via Risk Extrapolation (REx)
David Krueger,Ethan Caballero,Joern-Henrik Jacobsen,Amy Zhang,Jonathan Binas,Dinghuai Zhang,Rémi Le Priol,Aaron Courville +7 more
TL;DR: This work introduces the principle of Risk Extrapolation (REx), and shows conceptually how this principle enables extrapolation, and demonstrates the effectiveness and scalability of instantiations of REx on various OoD generalization tasks.
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Deep Subdomain Adaptation Network for Image Classification
TL;DR: This work presents a deep subdomain adaptation network (DSAN) that learns a transfer network by aligning the relevant subdomain distributions of domain-specific layer activations across different domains based on a local maximum mean discrepancy (LMMD).
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