mlsanityData to 7 Oct 2026

Partial Domain Adaptation Using Selective Representation Learning For Class-Weight Computation

arXiv 2101.02275 ↗First posted 6 Jan 2021cs.CVcited by 4

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Abstract (arXiv)

The generalization power of deep-learning models is dependent on rich-labelled data. This supervision using large-scaled annotated information is restrictive in most real-world scenarios where data collection and their annotation involve huge cost. Various domain adaptation techniques exist in literature that bridge this distribution discrepancy. However, a majority of these models require the label sets of both the domains to be identical. To tackle a more practical and challenging scenario, we formulate the problem statement from a partial domain adaptation perspective, where the source label set is a super set of the target label set. Driven by the motivation that image styles are private to each domain, in this work, we develop a method that identifies outlier classes exclusively from image content information and train a label classifier exclusively on class-content from source images. Additionally, elimination of negative transfer of samples from classes private to the source domain is achieved by transforming the soft class-level weights into two clusters, 0 (outlier source classes) and 1 (shared classes) by maximizing the between-cluster variance between them.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 13 references that are in the map. First posted on arXiv 6 Jan 2021.

Builds on

11 of 13 references.

References · 11

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  3. 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,323
  4. 2017Adversarial Discriminative Domain Adaptationcited by 1,309
  5. 2017Deep Hashing Network for Unsupervised Domain Adaptationcited by 1,181
  6. 2016Unsupervised Domain Adaptation with Residual Transfer Networkscited by 340
  7. 2016Domain Separation Networkscited by 317
  8. 2018Partial Adversarial Domain Adaptationcited by 127
  9. 2018Importance Weighted Adversarial Nets for Partial Domain Adaptationcited by 90
  10. 2019Learning to Transfer Examples for Partial Domain Adaptationcited by 86
  11. 2017Partial Transfer Learning with Selective Adversarial Networkscited by 84

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Cited by year

Citing papers per year: 2022: 2; 2023: 2.

4 papers.

  1. 2022Coupling Adversarial Learning with Selective Voting Strategy for Distribution Alignment in Partial Domain Adaptationcited by 3
  2. 2022Domain-Invariant Feature Alignment Using Variational Inference For Partial Domain Adaptationcited by 0
  3. 2023A Robust Negative Learning Approach to Partial Domain Adaptation Using Source Prototypescited by 0
  4. 2023Robust Class-Conditional Distribution Alignment for Partial Domain Adaptationcited by 0