Open AccessProceedings Article
Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation
Boqing Gong,Kristen Grauman,Fei Sha +2 more
- 16 Jun 2013
- pp 222-230
TL;DR: This paper automatically discovers the existence of landmarks and uses them to bridge the source to the target by constructing provably easier auxiliary domain adaptation tasks, and shows how this composition can be optimized discriminatively without requiring labels from the target domain.
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Abstract: Learning domain-invariant features is of vital importance to unsupervised domain adaptation, where classifiers trained on the source domain need to be adapted to a different target domain for which no labeled examples are available. In this paper, we propose a novel approach for learning such features. The central idea is to exploit the existence of landmarks, which are a subset of labeled data instances in the source domain that are distributed most similarly to the target domain. Our approach automatically discovers the landmarks and use them to bridge the source to the target by constructing provably easier auxiliary domain adaptation tasks. The solutions of those auxiliary tasks form the basis to compose invariant features for the original task. We show how this composition can be optimized discriminatively without requiring labels from the target domain. We validate the method on standard benchmark datasets for visual object recognition and sentiment analysis of text. Empirical results show the proposed method outperforms the state-of-the-art significantly.
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Citations
Progressive domain adaptation for detecting hate speech on social media with small training set and its application to COVID-19 concerned posts
TL;DR: In this article, a novel concept of unsupervised progressive domain adaptation based on a deep-learning language model generated through multiple text datasets was proposed for hate speech and fear detection on the tweets collection during COVID-19.
•Dissertation
Combating fake news with adversarial domain adaptation and neural models
Brian Xu
- 01 Jan 2019
TL;DR: A model is yielded which has state of the art performance on FNC target data by using FEVER source data coupled with adversarial domain adaptation and this thesis focuses on two main tasks: fact checking and stance detection.
2
•Posted Content
Generation for Adaption: A GAN-Based Approach for 3D Domain Adaption with Point Cloud Data.
Junxuan Huang,Chunming Qiao +1 more
TL;DR: Instead of aligning features between source data and target data, the authors proposed a method that use a Generative Adversarial Network (GAN) to generate synthetic data from the source domain so that the output is close to the target domain.
2
Unsupervised Domain Adaptation by regularizing Softmax Activation
Cunbin Gui,Jiani Hu +1 more
- 01 Aug 2018
TL;DR: This paper proposes to use MMD to regularize the softmax predictions to learn more transferable features by backpropagating and bridges the domain-invariant feature and the classifying feature, which is before the final softmax, by a Residual-block.
2
References
ImageNet: A large-scale hierarchical image database
Jia Deng,Wei Dong,Richard Socher,Li-Jia Li,Kai Li,Li Fei-Fei +5 more
- 20 Jun 2009
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
LabelMe: A Database and Web-Based Tool for Image Annotation
TL;DR: In this article, a large collection of images with ground truth labels is built to be used for object detection and recognition research, such data is useful for supervised learning and quantitative evaluation.
A theory of learning from different domains
Shai Ben-David,John Blitzer,Koby Crammer,Alex Kulesza,Fernando Pereira,Jennifer Wortman Vaughan +5 more
TL;DR: A classifier-induced divergence measure that can be estimated from finite, unlabeled samples from the domains and shows how to choose the optimal combination of source and target error as a function of the divergence, the sample sizes of both domains, and the complexity of the hypothesis class.
Domain Adaptation via Transfer Component Analysis
TL;DR: This work proposes a novel dimensionality reduction framework for reducing the distance between domains in a latent space for domain adaptation and proposes both unsupervised and semisupervised feature extraction approaches, which can dramatically reduce thedistance between domain distributions by projecting data onto the learned transfer components.