6,595 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
Discriminative Adversarial Domain Adaptation
Given labeled instances on a source domain and unlabeled ones on a target
domain, unsupervised domain adaptation aims to learn a task classifier that can
well classify target instances. Recent advances rely on domain-adversarial
training of deep networks to learn domain-invariant features. However, due to
an issue of mode collapse induced by the separate design of task and domain
classifiers, these methods are limited in aligning the joint distributions of
feature and category across domains. To overcome it, we propose a novel
adversarial learning method termed Discriminative Adversarial Domain Adaptation
(DADA). Based on an integrated category and domain classifier, DADA has a novel
adversarial objective that encourages a mutually inhibitory relation between
category and domain predictions for any input instance. We show that under
practical conditions, it defines a minimax game that can promote the joint
distribution alignment. Except for the traditional closed set domain
adaptation, we also extend DADA for extremely challenging problem settings of
partial and open set domain adaptation. Experiments show the efficacy of our
proposed methods and we achieve the new state of the art for all the three
settings on benchmark datasets.Comment: 18 pages, 10 figures, 12 tables, accepted by AAAI-2
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
Segmentation and Unsupervised Adversarial Domain Adaptation Between Medical Imaging Modalities
Segmenting and labelling tumors in multimodal medical imaging are often vital parts of diagnostics and can in many cases be very labor intensive for clinicians. The effort in advancing time-saving methods in the medical health sector might be of great help for busy clinicians and can maybe even save lives.
Furthermore, creating methods that generically, accurately and successfully process unlabelled data would be a major breakthrough in deep learning.
This thesis aims to address both these challenges by exploring and improving current methods involving adversarial discriminative domain adaptation (ADDA) on multimodal imaging, and address weaknesses, not only in ADDA, but also in the general adversarial discriminative cases.
More specifically, this thesis
- applies convolutional neural networks to segment soft tissue sarcomas in PET, CT and MRI modalities, and to the author's best knowledge achieves state-of-the-art results,
- explores unsupervised adversarial discriminative domain adaptation on segmentation of soft tissue sarcoma tumors between permutations of PET, CT and MRI and
- demonstrates weaknesses in state-of-the-art adversarial discriminative training, and finally
- improves and provides groundwork for further research on said techniques.
Additionally, the thesis will also provide strong fundamental background for applying ADDA for use in medical modalities, including a solid introduction to deep learning in medical imaging, both from a theoretical and practical aspect
- …