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Evaluation of Deep Neural Network Domain Adaptation Techniques for Image Recognition

arXiv 2109.13420 ↗First posted 28 Sep 2021cs.CV cs.AIcited by 1

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Abstract (arXiv)

It has been well proved that deep networks are efficient at extracting features from a given (source) labeled dataset. However, it is not always the case that they can generalize well to other (target) datasets which very often have a different underlying distribution. In this report, we evaluate four different domain adaptation techniques for image classification tasks: DeepCORAL, DeepDomainConfusion, CDAN and CDAN+E. These techniques are unsupervised given that the target dataset dopes not carry any labels during training phase. We evaluate model performance on the office-31 dataset. A link to the github repository of this report can be found here: https://github.com/agrija9/Deep-Unsupervised-Domain-Adaptation.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 28 Sep 2021.

Builds on

15 of 22 references.

References · 15

  1. 2014Conditional Generative Adversarial Netscited by 2,162
  2. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,917
  3. 2017Adversarial Discriminative Domain Adaptationcited by 1,311
  4. 2016Deep CORAL: Correlation Alignment for Deep Domain Adaptationcited by 1,200
  5. 2014Deep Domain Confusion: Maximizing for Domain Invariancecited by 600
  6. 2019Semi-supervised Domain Adaptation via Minimax Entropycited by 335
  7. 2015Simultaneous Deep Transfer Across Domains and Taskscited by 206
  8. 2016Correlation Alignment for Unsupervised Domain Adaptationcited by 147
  9. 2017Incremental Adversarial Domain Adaptation for Continually Changing Environmentscited by 52
  10. 2019Few-shot Adaptive Faster R-CNNcited by 42
  11. 2019d-SNE: Domain Adaptation using Stochastic Neighborhood Embeddingcited by 40
  12. 2017Zero-Shot Deep Domain Adaptationcited by 35
3 more
  1. 2013One-Shot Adaptation of Supervised Deep Convolutional Modelscited by 9
  2. 2019Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaborationcited by 8
  3. 2019Batch weight for domain adaptation with mass shiftcited by 5

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Citing papers per year: 2023: 1.

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