8,998 research outputs found
Adaptation Algorithm and Theory Based on Generalized Discrepancy
We present a new algorithm for domain adaptation improving upon a discrepancy
minimization algorithm previously shown to outperform a number of algorithms
for this task. Unlike many previous algorithms for domain adaptation, our
algorithm does not consist of a fixed reweighting of the losses over the
training sample. We show that our algorithm benefits from a solid theoretical
foundation and more favorable learning bounds than discrepancy minimization. We
present a detailed description of our algorithm and give several efficient
solutions for solving its optimization problem. We also report the results of
several experiments showing that it outperforms discrepancy minimization
Improved Techniques for Adversarial Discriminative Domain Adaptation
Adversarial discriminative domain adaptation (ADDA) is an efficient framework
for unsupervised domain adaptation in image classification, where the source
and target domains are assumed to have the same classes, but no labels are
available for the target domain. We investigate whether we can improve
performance of ADDA with a new framework and new loss formulations. Following
the framework of semi-supervised GANs, we first extend the discriminator output
over the source classes, in order to model the joint distribution over domain
and task. We thus leverage on the distribution over the source encoder
posteriors (which is fixed during adversarial training) and propose maximum
mean discrepancy (MMD) and reconstruction-based loss functions for aligning the
target encoder distribution to the source domain. We compare and provide a
comprehensive analysis of how our framework and loss formulations extend over
simple multi-class extensions of ADDA and other discriminative variants of
semi-supervised GANs. In addition, we introduce various forms of regularization
for stabilizing training, including treating the discriminator as a denoising
autoencoder and regularizing the target encoder with source examples to reduce
overfitting under a contraction mapping (i.e., when the target per-class
distributions are contracting during alignment with the source). Finally, we
validate our framework on standard domain adaptation datasets, such as SVHN and
MNIST. We also examine how our framework benefits recognition problems based on
modalities that lack training data, by introducing and evaluating on a
neuromorphic vision sensing (NVS) sign language recognition dataset, where the
source and target domains constitute emulated and real neuromorphic spike
events respectively. Our results on all datasets show that our proposal
competes or outperforms the state-of-the-art in unsupervised domain adaptation.Comment: To appear in IEEE Transactions on Image Processin
- …