# On Minimum Discrepancy Estimation for Deep Domain Adaptation

arXiv: https://arxiv.org/abs/1901.00282 · HTML: https://mlsanity.com/paper/1901.00282/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2019.

## Built on by (most cited first, 30 of 34)

- [ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data](https://mlsanity.com/paper/2203.08321/) (2022)
- [Contrastive Learning for Unsupervised Domain Adaptation of Time Series](https://mlsanity.com/paper/2206.06243/) (2022)
- [A Survey of Unsupervised Domain Adaptation for Visual Recognition](https://mlsanity.com/paper/2112.06745/) (2021)
- [Self-supervised Autoregressive Domain Adaptation for Time Series Data](https://mlsanity.com/paper/2111.14834/) (2021)
- [ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training](https://mlsanity.com/paper/2107.04470/) (2021)
- [Source-Free Domain Adaptation with Temporal Imputation for Time Series Data](https://mlsanity.com/paper/2307.07542/) (2023)
- [Conditional Extreme Value Theory for Open Set Video Domain Adaptation](https://mlsanity.com/paper/2109.00522/) (2021)
- [A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges](https://mlsanity.com/paper/2205.03824/) (2022)
- [SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shifts](https://mlsanity.com/paper/2506.12007/) (2025)
- [POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning](https://mlsanity.com/paper/2312.12276/) (2023)
- [Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation](https://mlsanity.com/paper/2406.02635/) (2024)
- [Overcoming Saturation in Density Ratio Estimation by Iterated Regularization](https://mlsanity.com/paper/2402.13891/) (2024)
- [Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies](https://mlsanity.com/paper/2503.23775/) (2025)
- [Efficient Pre-trained Features and Recurrent Pseudo-Labeling in Unsupervised Domain Adaptation](https://mlsanity.com/paper/2104.13486/) (2021)
- [Discriminative Domain-Invariant Adversarial Network for Deep Domain Generalization](https://mlsanity.com/paper/2108.08995/) (2021)
- [Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2409.19635/) (2024)
- [Deep Domain Generalization with Feature-norm Network](https://mlsanity.com/paper/2104.13581/) (2021)
- [Deep Least Squares Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2111.02207/) (2021)
- [LogoRA: Local-Global Representation Alignment for Robust Time Series Classification](https://mlsanity.com/paper/2409.12169/) (2024)
- [Preserving Semantic Consistency in Unsupervised Domain Adaptation Using Generative Adversarial Networks](https://mlsanity.com/paper/2104.13725/) (2021)
- [Correlated Adversarial Joint Discrepancy Adaptation Network](https://mlsanity.com/paper/2105.08808/) (2021)
- [Binary Losses for Density Ratio Estimation](https://mlsanity.com/paper/2407.01371/) (2024)
- [Temporal Restoration and Spatial Rewiring for Source-Free Multivariate Time Series Domain Adaptation](https://mlsanity.com/paper/2505.21525/) (2025)
- [Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data](https://mlsanity.com/paper/2307.03347/) (2023)
- [Time and Frequency Synergy for Source-Free Time-Series Domain Adaptations](https://mlsanity.com/paper/2410.17511/) (2024)
- [Time Series Domain Adaptation via Latent Invariant Causal Mechanism](https://mlsanity.com/paper/2502.16637/) (2025)
- [STDA-Net: Spectrogram-Based Domain Adaptation for cross-dataset Sleep Stage Classification](https://mlsanity.com/paper/2605.06736/) (2026)
- [Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification](https://mlsanity.com/paper/2209.13420/) (2022)
- [Deep Feature Registration for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2310.16100/) (2023)
- [Learning Compositional Transferability of Time Series for Source-Free Domain Adaptation](https://mlsanity.com/paper/2504.14994/) (2025)

## Abstract (arXiv)

In the presence of large sets of labeled data, Deep Learning (DL) has accomplished extraordinary triumphs in the avenue of computer vision, particularly in object classification and recognition tasks. However, DL cannot always perform well when the training and testing images come from different distributions or in the presence of domain shift between training and testing images. They also suffer in the absence of labeled input data. Domain adaptation (DA) methods have been proposed to make up the poor performance due to domain shift. In this paper, we present a new unsupervised deep domain adaptation method based on the alignment of second order statistics (covariances) as well as maximum mean discrepancy of the source and target data with a two stream Convolutional Neural Network (CNN). We demonstrate the ability of the proposed approach to achieve state-of the-art performance for image classification on three benchmark domain adaptation datasets: Office-31 [27], Office-Home [37] and Office-Caltech [8].