mlsanityData to 7 Oct 2026

ADAPT : Awesome Domain Adaptation Python Toolbox

arXiv 2107.03049 ↗First posted 7 Jul 2021cs.LGcited by 13

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Abstract (arXiv)

In this paper, we introduce the ADAPT library, an open source Python API providing the implementation of the main transfer learning and domain adaptation methods. The library is designed with a user friendly approach to facilitate the access to domain adaptation for a wide public. ADAPT is compatible with scikit-learn and TensorFlow and a full documentation is proposed online https://adapt-python.github.io/adapt/ with a substantial gallery of examples.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 18 of its 51 references that are in the map. First posted on arXiv 7 Jul 2021.

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18 of 51 references.

References · 18

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  3. 2017Adversarial Discriminative Domain Adaptationcited by 1,309
  4. 2016Deep CORAL: Correlation Alignment for Deep Domain Adaptationcited by 1,194
  5. 2020In Search of Lost Domain Generalizationcited by 1,042
  6. 2020Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptationcited by 844
  7. 2017Maximum Classifier Discrepancy for Unsupervised Domain Adaptationcited by 682
  8. 2015Optimal Transport for Domain Adaptationcited by 457
  9. 2015Return of Frustratingly Easy Domain Adaptationcited by 389
  10. 2019Bridging Theory and Algorithm for Domain Adaptationcited by 332
  11. 2017Unified Deep Supervised Domain Adaptation and Generalizationcited by 322
  12. 2020Domain Adaptive Ensemble Learningcited by 156
6 more
  1. 2018Domain Adversarial for Acoustic Emotion Recognitioncited by 25
  2. 2020Adversarial Weighting for Domain Adaptation in Regressioncited by 19
  3. 2017Understanding the Mechanisms of Deep Transfer Learning for Medical Imagescited by 10
  4. 2021Nearest Neighbor-based Importance Weightingcited by 9
  5. 2015Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random Forestscited by 6
  6. 2019Transfer Learning Toolkit: Primers and Benchmarkscited by 1

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Citing papers per year: 2022: 3; 2023: 6; 2024: 4.

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  1. 2023Better, Not Just More: Data-Centric Machine Learning for Earth Observationcited by 19
  2. 2022Fast and Accurate Importance Weighting for Correcting Sample Biascited by 5
  3. 2023Nearest Neighbor Sampling for Covariate Shift Adaptationcited by 5
  4. 2024A General Approach for Determining Applicability Domain of Machine Learning Modelscited by 5
  5. 2023Unsupervised domain adaptation by learning using privileged informationcited by 3
  6. 2022Detecting train driveshaft damages using accelerometer signals and Differential Convolutional Neural Networkscited by 1
  7. 2023Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case Studycited by 1
  8. 2024Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagerycited by 1
  9. 2022PyTorch Adaptcited by 0
  10. 2023biquality-learn: a Python library for Biquality Learningcited by 0
  11. 2023Some notes concerning a generalized KMM-type optimization method for density ratio estimationcited by 0
  12. 2024Sample Selection Bias in Machine Learning for Healthcarecited by 0
1 more
  1. 2024Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regionscited by 0