Research map / Label-efficient and robust learning / Domain adaptation and generalization
AdaGraph: Unifying Predictive and Continuous Domain Adaptation Through Graphs
Check your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use AdaGraph with target metadata instead of the source-only baseline | CompCars predictive domain adaptation · CompCars | average accuracy: 54.0 -> 60.1 | Helped |
| Use image-based domain assignment instead of target metadata | AdaGraph · CompCars | average accuracy: 60.1 -> 60.8 | Helped |
| Refine AdaGraph using incoming target data | CompCars predictive domain adaptation · CompCars | average accuracy: 60.1 -> 60.9 | Helped |
| Use graph-based domain-specific batch normalization | Source-only ResNet-18 · CompCars | average accuracy: 56.8 -> 65.1 | Helped |
| Refine the AdaGraph target model using incoming data | AdaGraph ResNet-18 · CompCars | average accuracy: 65.1 -> 66.7 | Helped |
| Refine target batch-normalization statistics (ablation) | Baseline SVM · CarEvolution | accuracy: 39.7 -> 46.5 | Helped |
| Add entropy-loss refinement of scale and bias to statistics refinement (ablation) | Baseline SVM · CarEvolution | accuracy: 46.5 -> 47.3 | Helped |
| Refine the source-only target model using incoming target data (ablation) | Portraits baseline · Portraits (continuous adaptation) | accuracy: 81.9 -> 87.3 | Helped |
| Add entropy-loss refinement of scale and bias to statistics refinement (ablation) | Portraits baseline · Portraits (continuous adaptation) | accuracy: 87.3 -> 88.1 | Helped |
| Predict target BN statistics with the graph (ablation) | Source-only ResNet-18 · Portraits (across decades) | average classification accuracy: 82.3 -> 86.3 | Helped |
| Predict target BN statistics with the graph (ablation) | Source-only ResNet-18 · Portraits (across regions) | average classification accuracy: 89.2 -> 91.6 | Helped |
| Add separately trained scale and bias parameters to graph-predicted BN statistics (ablation) | AdaGraph BN · Portraits (across decades) | average classification accuracy: 86.3 -> 86.0 | Hurt |
Closest papers by meaning
- AnyDA: Anytime Domain Adaptation (ICLR 2023 · similarity 0.89)
- A General Representation-Based Approach to Multi-Source Domain Adaptation (ICML 2025 · similarity 0.89)
- Visual Domain Adaptation by Consensus-Based Transfer to Intermediate Domain (AAAI 2020 · similarity 0.89)
- Learning an Invariant Hilbert Space for Domain Adaptation (CVPR 2017 · similarity 0.89)
- HiGDA: Hierarchical Graph of Nodes to Learn Local-to-Global Topology for Semi-Supervised Domain Adaptation (AAAI 2025 · similarity 0.87)
- Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation (NeurIPS 2024 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.