Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders

• URL: http://arxiv.org/abs/2008.01214v1
• Date: Mon, 3 Aug 2020 21:48:50 GMT
• Title: Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders
• Authors: Qian Wang, Toby P. Breckon
• Abstract summary: We present a novel Conditional Coupled Variational Autoencoder (CCVAE) which can generate synthetic target domain features for unseen classes from their source domain counterparts. Experiments have been conducted on three domain adaptation datasets including a bespoke X-ray security checkpoint dataset to simulate a real-world application in aviation security.
• Score: 23.18781318003242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain adaptation approaches aim to exploit useful information from the source domain where supervised learning examples are easier to obtain to address a learning problem in the target domain where there is no or limited availability of such examples. In classification problems, domain adaptation has been studied under varying supervised, unsupervised and semi-supervised conditions. However, a common situation when the labelled samples are available for a subset of target domain classes has been overlooked. In this paper, we formulate this particular domain adaptation problem within a generalized zero-shot learning framework by treating the labelled source domain samples as semantic representations for zero-shot learning. For this particular problem, neither conventional domain adaptation approaches nor zero-shot learning algorithms directly apply. To address this generalized zero-shot domain adaptation problem, we present a novel Coupled Conditional Variational Autoencoder (CCVAE) which can generate synthetic target domain features for unseen classes from their source domain counterparts. Extensive experiments have been conducted on three domain adaptation datasets including a bespoke X-ray security checkpoint dataset to simulate a real-world application in aviation security. The results demonstrate the effectiveness of our proposed approach both against established benchmarks and in terms of real-world applicability.

Related papers

• Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning [4.921899151930171]
  Unsupervised continual domain shift learning is a significant challenge in real-world applications. We propose Complementary Domain Adaptation and Generalization (CoDAG), a simple yet effective learning framework. Our approach is model-agnostic, meaning that it is compatible with any existing domain adaptation and generalization algorithms.
  arXiv  Detail & Related papers  (2023-03-28T09:05:15Z)
• Self-Paced Learning for Open-Set Domain Adaptation [50.620824701934]
  Traditional domain adaptation methods presume that the classes in the source and target domains are identical. Open-set domain adaptation (OSDA) addresses this limitation by allowing previously unseen classes in the target domain. We propose a novel framework based on self-paced learning to distinguish common and unknown class samples.
  arXiv  Detail & Related papers  (2023-03-10T14:11:09Z)
• Labeling Where Adapting Fails: Cross-Domain Semantic Segmentation with Point Supervision via Active Selection [81.703478548177]
  Training models dedicated to semantic segmentation require a large amount of pixel-wise annotated data. Unsupervised domain adaptation approaches aim at aligning the feature distributions between the labeled source and the unlabeled target data. Previous works attempted to include human interactions in this process under the form of sparse single-pixel annotations in the target data. We propose a new domain adaptation framework for semantic segmentation with annotated points via active selection.
  arXiv  Detail & Related papers  (2022-06-01T01:52:28Z)
• Joint Distribution Alignment via Adversarial Learning for Domain Adaptive Object Detection [11.262560426527818]
  Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data. Recently, mainstream approaches perform this task through adversarial learning, yet still suffer from two limitations. We propose a joint adaptive detection framework (JADF) to address the above challenges.
  arXiv  Detail & Related papers  (2021-09-19T00:27:08Z)
• Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation [74.71931918541748]
  We propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA. We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process. We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-03T01:33:14Z)
• Inferring Latent Domains for Unsupervised Deep Domain Adaptation [54.963823285456925]
  Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available. This paper introduces a novel deep architecture which addresses the problem of UDA by automatically discovering latent domains in visual datasets. We evaluate our approach on publicly available benchmarks, showing that it outperforms state-of-the-art domain adaptation methods.
  arXiv  Detail & Related papers  (2021-03-25T14:33:33Z)
• Domain Adaptation with Incomplete Target Domains [61.68950959231601]
  We propose an Incomplete Data Imputation based Adversarial Network (IDIAN) model to address this new domain adaptation challenge. In the proposed model, we design a data imputation module to fill the missing feature values based on the partial observations in the target domain. We conduct experiments on both cross-domain benchmark tasks and a real world adaptation task with imperfect target domains.
  arXiv  Detail & Related papers  (2020-12-03T00:07:40Z)
• Learning causal representations for robust domain adaptation [31.261956776418618]
  In many real-world applications, target domain data may not always be available. In this paper, we study the cases where at the training phase the target domain data is unavailable. We propose a novel Causal AutoEncoder (CAE), which integrates deep autoencoder and causal structure learning into a unified model.
  arXiv  Detail & Related papers  (2020-11-12T11:24:03Z)
• Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation [109.73983088432364]
  We propose the first method that aims to simultaneously learn invariant representations and risks under the setting of semi-supervised domain adaptation (Semi-DA) We introduce the LIRR algorithm for jointly textbfLearning textbfInvariant textbfRepresentations and textbfRisks.
  arXiv  Detail & Related papers  (2020-10-09T15:42:35Z)
• Enlarging Discriminative Power by Adding an Extra Class in Unsupervised Domain Adaptation [5.377369521932011]
  We propose an idea of empowering the discriminativeness: Adding a new, artificial class and training the model on the data together with the GAN-generated samples of the new class. Our idea is highly generic so that it is compatible with many existing methods such as DANN, VADA, and DIRT-T.
  arXiv  Detail & Related papers  (2020-02-19T07:58:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.