# Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks

arXiv: https://arxiv.org/abs/1907.03389 · HTML: https://mlsanity.com/paper/1907.03389/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

## Built on by (most cited first, 30 of 37)

- [Advances and Challenges in Meta-Learning: A Technical Review](https://mlsanity.com/paper/2307.04722/) (2023)
- [Balancing Discriminability and Transferability for Source-Free Domain Adaptation](https://mlsanity.com/paper/2206.08009/) (2022)
- [Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation](https://mlsanity.com/paper/2104.00808/) (2021)
- [A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm](https://mlsanity.com/paper/2310.12244/) (2023)
- [Semantic Image Segmentation: Two Decades of Research](https://mlsanity.com/paper/2302.06378/) (2023)
- [Leveraging Self-Supervision for Cross-Domain Crowd Counting](https://mlsanity.com/paper/2103.16291/) (2021)
- [CoNMix for Source-free Single and Multi-target Domain Adaptation](https://mlsanity.com/paper/2211.03876/) (2022)
- [Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation](https://mlsanity.com/paper/2302.02561/) (2023)
- [Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey](https://mlsanity.com/paper/2112.03241/) (2021)
- [FastSurferVINN: Building Resolution-Independence into Deep Learning Segmentation Methods -- A Solution for HighRes Brain MRI](https://mlsanity.com/paper/2112.09654/) (2021)
- [Graph-Relational Domain Adaptation](https://mlsanity.com/paper/2202.03628/) (2022)
- [Domain-Specific Bias Filtering for Single Labeled Domain Generalization](https://mlsanity.com/paper/2110.00726/) (2021)
- [Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2108.06962/) (2021)
- [Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains](https://mlsanity.com/paper/2101.07308/) (2021)
- [One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video](https://mlsanity.com/paper/2103.12988/) (2021)
- [Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2204.11667/) (2022)
- [Generalized Domain Adaptation](https://mlsanity.com/paper/2106.01656/) (2021)
- [Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering](https://mlsanity.com/paper/2309.00528/) (2023)
- [Gradual Domain Adaptation via Self-Training of Auxiliary Models](https://mlsanity.com/paper/2106.09890/) (2021)
- [Amplitude Spectrum Transformation for Open Compound Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2202.04287/) (2022)
- [Towards Instance-adaptive Inference for Federated Learning](https://mlsanity.com/paper/2308.06051/) (2023)
- [Incremental Multi-Target Domain Adaptation for Object Detection with Efficient Domain Transfer](https://mlsanity.com/paper/2104.06476/) (2021)
- [Towards Robust Semantic Segmentation of Accident Scenes via Multi-Source Mixed Sampling and Meta-Learning](https://mlsanity.com/paper/2203.10395/) (2022)
- [Taxonomy-Structured Domain Adaptation](https://mlsanity.com/paper/2306.07874/) (2023)
- [Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation](https://mlsanity.com/paper/2107.02718/) (2021)
- [COSMo: CLIP Talks on Open-Set Multi-Target Domain Adaptation](https://mlsanity.com/paper/2409.00397/) (2024)
- [Knowledge Distillation for Multi-Target Domain Adaptation in Real-Time Person Re-Identification](https://mlsanity.com/paper/2205.06237/) (2022)
- [Semantic Segmentation in Multiple Adverse Weather Conditions with Domain Knowledge Retention](https://mlsanity.com/paper/2401.07459/) (2024)
- [Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation](https://mlsanity.com/paper/2210.01578/) (2022)
- [Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation](https://mlsanity.com/paper/2302.01516/) (2023)

## Abstract (arXiv)

(Unsupervised) Domain Adaptation (DA) seeks for classifying target instances when solely provided with source labeled and target unlabeled examples for training. Learning domain-invariant features helps to achieve this goal, whereas it underpins unlabeled samples drawn from a single or multiple explicit target domains (Multi-target DA). In this paper, we consider a more realistic transfer scenario: our target domain is comprised of multiple sub-targets implicitly blended with each other, so that learners could not identify which sub-target each unlabeled sample belongs to. This Blending-target Domain Adaptation (BTDA) scenario commonly appears in practice and threatens the validities of most existing DA algorithms, due to the presence of domain gaps and categorical misalignments among these hidden sub-targets. To reap the transfer performance gains in this new scenario, we propose Adversarial Meta-Adaptation Network (AMEAN). AMEAN entails two adversarial transfer learning processes. The first is a conventional adversarial transfer to bridge our source and mixed target domains. To circumvent the intra-target category misalignment, the second process presents as ``learning to adapt'': It deploys an unsupervised meta-learner receiving target data and their ongoing feature-learning feedbacks, to discover target clusters as our ``meta-sub-target'' domains. These meta-sub-targets auto-design our meta-sub-target DA loss, which empirically eliminates the implicit category mismatching in our mixed target. We evaluate AMEAN and a variety of DA algorithms in three benchmarks under the BTDA setup. Empirical results show that BTDA is a quite challenging transfer setup for most existing DA algorithms, yet AMEAN significantly outperforms these state-of-the-art baselines and effectively restrains the negative transfer effects in BTDA.