Supervised Domain Adaptation using Graph Embedding
- URL: http://arxiv.org/abs/2003.04063v2
- Date: Tue, 8 Sep 2020 09:35:19 GMT
- Title: Supervised Domain Adaptation using Graph Embedding
- Authors: Lukas Hedegaard Morsing, Omar Ali Sheikh-Omar and Alexandros Iosifidis
- Abstract summary: 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.
- Score: 86.3361797111839
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Getting deep convolutional neural networks to perform well requires a large
amount of training data. When the available labelled data is small, it is often
beneficial to use transfer learning to leverage a related larger dataset
(source) in order to improve the performance on the small dataset (target).
Among the transfer learning approaches, domain adaptation methods assume that
distributions between the two domains are shifted and attempt to realign them.
In this paper, we consider the domain adaptation problem from the perspective
of dimensionality reduction and propose a generic framework based on graph
embedding. Instead of solving the generalised eigenvalue problem, we formulate
the graph-preserving criterion as a loss in the neural network and learn a
domain-invariant feature transformation in an end-to-end fashion. We show that
the proposed approach leads to a powerful Domain Adaptation framework; a simple
LDA-inspired instantiation of the framework leads to state-of-the-art
performance on two of the most widely used Domain Adaptation benchmarks,
Office31 and MNIST to USPS datasets.
Related papers
- AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation [13.472532378889264]
In semi-supervised domain adaptation (SSDA), the model aims to leverage partially labeled target domain data along with a large amount of labeled source domain data.
This paper proposes a graph learning perspective (AGLP) for semi-supervised domain adaptation.
We apply the graph convolutional network to the instance graph which allows structural information to propagate along the weighted graph edges.
arXiv Detail & Related papers (2024-11-20T09:41:41Z) - Progressive Conservative Adaptation for Evolving Target Domains [76.9274842289221]
Conventional domain adaptation typically transfers knowledge from a source domain to a stationary target domain.
Restoring and adapting to such target data results in escalating computational and resource consumption over time.
We propose a simple yet effective approach, termed progressive conservative adaptation (PCAda)
arXiv Detail & Related papers (2024-02-07T04:11:25Z) - Domain Adaptation Principal Component Analysis: base linear method for
learning with out-of-distribution data [55.41644538483948]
Domain adaptation is a popular paradigm in modern machine learning.
We present a method called Domain Adaptation Principal Component Analysis (DAPCA)
DAPCA finds a linear reduced data representation useful for solving the domain adaptation task.
arXiv Detail & Related papers (2022-08-28T21:10:56Z) - Feed-Forward Latent Domain Adaptation [17.71179872529747]
We study a new highly-practical problem setting that enables resource-constrained edge devices to adapt a pre-trained model to their local data distributions.
Considering limitations of edge devices, we aim to only use a pre-trained model and adapt it in a feed-forward way, without using back-propagation and without access to the source data.
Our solution is to meta-learn a network capable of embedding the mixed-relevance target dataset and dynamically adapting inference for target examples using cross-attention.
arXiv Detail & Related papers (2022-07-15T17:37:42Z) - Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training
for Road Segmentation of Remote Sensing Images [93.50240389540252]
Road segmentation from remote sensing images is a challenging task with wide ranges of application potentials.
We propose a novel stagewise domain adaptation model called RoadDA to address the domain shift (DS) issue in this field.
Experiment results on two benchmarks demonstrate that RoadDA can efficiently reduce the domain gap and outperforms state-of-the-art methods.
arXiv Detail & Related papers (2021-08-28T09:29:14Z) - Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class
Distributions [9.581605678437032]
This work addresses the problem of unsupervised domain adaptation for LiDAR semantic segmentation models.
Our approach combines novel ideas on top of the current state-of-the-art approaches and yields new state-of-the-art results.
arXiv Detail & Related papers (2020-10-23T08:52:15Z) - Learning Domain-invariant Graph for Adaptive Semi-supervised Domain
Adaptation with Few Labeled Source Samples [65.55521019202557]
Domain adaptation aims to generalize a model from a source domain to tackle tasks in a related but different target domain.
Traditional domain adaptation algorithms assume that enough labeled data, which are treated as the prior knowledge are available in the source domain.
We propose a Domain-invariant Graph Learning (DGL) approach for domain adaptation with only a few labeled source samples.
arXiv Detail & Related papers (2020-08-21T08:13:25Z) - Sequential Domain Adaptation through Elastic Weight Consolidation for
Sentiment Analysis [3.1473798197405944]
We propose a model-independent framework - Sequential Domain Adaptation (SDA)
Our experiments show that the proposed framework enables simple architectures such as CNNs to outperform complex state-of-the-art models in domain adaptation of sentiment analysis (SA)
In addition, we observe that the effectiveness of a harder first Anti-Curriculum ordering of source domains leads to maximum performance.
arXiv Detail & Related papers (2020-07-02T15:21:56Z) - Towards Fair Cross-Domain Adaptation via Generative Learning [50.76694500782927]
Domain Adaptation (DA) targets at adapting a model trained over the well-labeled source domain to the unlabeled target domain lying in different distributions.
We develop a novel Generative Few-shot Cross-domain Adaptation (GFCA) algorithm for fair cross-domain classification.
arXiv Detail & Related papers (2020-03-04T23:25:09Z)
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.