Fast OT for Latent Domain Adaptation

• URL: http://arxiv.org/abs/2210.00479v1
• Date: Sun, 2 Oct 2022 10:25:12 GMT
• Title: Fast OT for Latent Domain Adaptation
• Authors: Siddharth Roheda, Ashkan Panahi, Hamid Krim
• Abstract summary: We propose an algorithm that uses optimal transport theory with a verifiably efficient and implementable solution to learn the best latent feature representation. This is achieved by minimizing the cost of transporting the samples from the target domain to the distribution of the source domain.
• Score: 25.915629674463286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we address the problem of unsupervised Domain Adaptation. The need for such an adaptation arises when the distribution of the target data differs from that which is used to develop the model and the ground truth information of the target data is unknown. We propose an algorithm that uses optimal transport theory with a verifiably efficient and implementable solution to learn the best latent feature representation. This is achieved by minimizing the cost of transporting the samples from the target domain to the distribution of the source domain.

Related papers

• Optimal Transport for Domain Adaptation through Gaussian Mixture Models [7.292229955481438]
  We propose a novel approach, where we model the data distributions through Gaussian mixture models. The optimal transport solution gives us a matching between source and target domain mixture components. We experiment with 2 domain adaptation benchmarks in fault diagnosis, showing that our methods have state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-03-18T09:32:33Z)
• MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation [98.09845149258972]
  We introduce active sample selection to assist domain adaptation regarding the semantic segmentation task. With only a little workload to manually annotate these samples, the distortion of the target-domain distribution can be effectively alleviated. A powerful semi-supervised domain adaptation strategy is proposed to alleviate the long-tail distribution problem.
  arXiv  Detail & Related papers  (2023-01-18T07:55:22Z)
• Distributionally Robust Domain Adaptation [12.02023514105999]
  Domain Adaptation (DA) has recently received significant attention due to its potential to adapt a learning model across source and target domains with mismatched distributions. In this paper, we propose DRDA, a distributionally robust domain adaptation method.
  arXiv  Detail & Related papers  (2022-10-30T17:29:22Z)
• Variational Model Perturbation for Source-Free Domain Adaptation [64.98560348412518]
  We introduce perturbations into the model parameters by variational Bayesian inference in a probabilistic framework. We demonstrate the theoretical connection to learning Bayesian neural networks, which proves the generalizability of the perturbed model to target domains.
  arXiv  Detail & Related papers  (2022-10-19T08:41:19Z)
• Source-Free Domain Adaptation via Distribution Estimation [106.48277721860036]
  Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data. In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation.
  arXiv  Detail & Related papers  (2022-04-24T12:22:19Z)
• Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection [79.89082006155135]
  Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the issue of domain shift. UDA methods try to align the source and target representations to improve the generalization on the target domain. The Source-Free Adaptation Domain (SFDA) setting aims to alleviate these concerns by adapting a source-trained model for the target domain without requiring access to the source data.
  arXiv  Detail & Related papers  (2022-03-29T17:50:43Z)
• KL Guided Domain Adaptation [88.19298405363452]
  Domain adaptation is an important problem and often needed for real-world applications. A common approach in the domain adaptation literature is to learn a representation of the input that has the same distributions over the source and the target domain. We show that with a probabilistic representation network, the KL term can be estimated efficiently via minibatch samples.
  arXiv  Detail & Related papers  (2021-06-14T22:24:23Z)
• Unsupervised Model Adaptation for Continual Semantic Segmentation [15.820660013260584]
  We develop an algorithm for adapting a semantic segmentation model that is trained using a labeled source domain to generalize well in an unlabeled target domain. We provide theoretical analysis and explain conditions under which our algorithm is effective. Experiments on benchmark adaptation task demonstrate our method achieves competitive performance even compared with joint UDA approaches.
  arXiv  Detail & Related papers  (2020-09-26T04:55:50Z)
• Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport [35.37752209765114]
  We propose a new approach to domain adaptation on unlabeled target datasets. We exploit the diversity of source distributions by tuning their weights to the target task at hand. Our method, named Weighted Joint Distribution Optimal Transport (WJDOT), aims at finding simultaneously an Optimal Transport-based alignment between the source and target distributions.
  arXiv  Detail & Related papers  (2020-06-23T12:33:30Z)
• Supervised Domain Adaptation using Graph Embedding [86.3361797111839]
  Domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. We propose a generic framework based on graph embedding. We show that the proposed approach leads to a powerful Domain Adaptation framework.
  arXiv  Detail & Related papers  (2020-03-09T12:25:13Z)

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.