Domain adaptation for object recognition: An unsupervised approach

By Raghuraman Gopalan, Ruonan Li, Ramalingam Chellappa. 2011

1,114 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

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Citations in the corpus

Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions (2015, background): “This is extended in [10] to the unsupervised setting where target labels are not available at training time.”
Semi-Supervised QA with Generative Domain-Adaptive Nets (2017, methodology): “There are also methods seeking a domain transition from the source domain to the target domain (Gong et al., 2012; Gopalan et al., 2011; Pan et al., 2011).”
Domain-Adversarial Training of Neural Networks (2015, background): “Note also that several approaches perform transition from the source to the target domain (Gopalan et al., 2011; Gong et al., 2012) by changing gradually the training distribution.”
Domain Adaptation with Conditional Transferable Components (2016, methodology): “To obtain the shared representation, some methods ﬁrstly create intermediate representations by projecting the original feature to a series of subspaces and then concatenate them (Gopalan et al., 2011; Gong et al., 2012).”
Unsupervised Domain Adaptation with Residual Transfer Networks (2016, background): “The goal of this paper is to design a deep neural network that enables learning of transfer classifiers y = fs (x) and y = ft (x) to close the source-target discrepancy, such that the expected target risk Rt (ft) = E(x,y)∼q [ft (x) 6= y] can be bounded by leveraging the source domain supervised…”
Few-Shot Adversarial Domain Adaptation (2017, background): “Some try to find a function which can map from the source domain to the target domain [47, 28, 19, 16, 11, 55, 52].”
On Learning the Geodesic Path for Incremental Learning (2021, methodology): “The projection to subspaces has been used previously in domain adaptation [12, 13] and few-shot learning [36].”
Deep Domain Confusion: Maximizing for Domain Invariance (2014, background): “In the last few years several methods for visual domain adaptation have been suggested to overcome this issue [10, 33, 2, 29, 25, 22, 17, 16, 19, 20], but were limited to shallow models.”
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks (2016, methodology): “This has been applied in various non-CNN based approaches [18, 6, 20] as well as the”
Domain Separation Networks (2016, methodology): “This has been applied in various non-CNN based approaches [9, 5, 10] as well as the recent CNN-based Correlation Alignment (CORAL) [26] algorithm which “recolors” whitened source features with the covariance of features from the target domain.”
Measuring the tendency of CNNs to Learn Surface Statistical Regularities (2017, background): “The problems of transfer learning [44, 37, 38] and domain adaptation [35, 10] both investigate the generalization ability of deep neural networks.”
Taskonomy: Disentangling Task Transfer Learning (2018, background): “Domain adaption seeks to render a function that is developed on a certain domain applicable to another [42, 96, 5, 77, 50, 24, 34].”
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping (2018, background): “. Feature-level domain adaptation focuses on learning domain-invariant features, either by learning a transformation of ﬁxed, pre-computed features between source and target domains [18], [19], [20], [21] or by learning a domain-invariant feature extractor, often represented by a convolutional neura”
Undoing the Damage of Dataset Bias (2012, background): “[9] propose a domain adaptation technique for an unsupervised setting, where data from the target domain is unlabeled.”
Efficient Learning of Domain-invariant Image Representations (2013, methodology): “Recently proposed feature adaptation methods [1, 2, 12, 13, 14, 15] offer a solution by learning a category-independent feature transform that maps target features into the source, pooling all training labels across categories.”
A Comprehensive Survey on Transfer Learning (2019, background): “GFK is closely related to a previous approach termed geodesic flow subspaces (GFSs) [103].”
Deep Hashing Network for Unsupervised Domain Adaptation (2017, background): “Manifold based approaches are also popular in domain adaptation for computer vision, where the subspace of a domain is treated as a point on the manifold and transformations are learned to align two domains [20, 23].”