# Domain Adaptation and Few-Shot Learning

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/domain-adaptation-and-few-shot-learning/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the advances in transfer learning and domain adaptation, including topics such as few-shot learning, unsupervised learning, representation learning, deep networks, meta-learning, visual recognition, semi-supervised learning, and clustering analysis. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t11307 |
| Works | 87 |

## Topic papers all

Showing 15 of 87.

- [One-shot learning of object categories](https://scholariq.org/papers/one-shot-learning-of-object-categories/)
- [A Decade Survey of Transfer Learning (2010–2020)](https://scholariq.org/papers/a-decade-survey-of-transfer-learning-2010-2020/)
- [A Comprehensive Survey of Image Augmentation Techniques for Deep Learning](https://scholariq.org/papers/a-comprehensive-survey-of-image-augmentation-techniques-for-deep-learning/)
- [Self-challenging Improves Cross-Domain Generalization](https://scholariq.org/papers/self-challenging-improves-cross-domain-generalization/)
- [CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise](https://scholariq.org/papers/cleannet-transfer-learning-for-scalable-image-classifier-training-with-label/)
- [BlockDrop: Dynamic Inference Paths in Residual Networks](https://scholariq.org/papers/blockdrop-dynamic-inference-paths-in-residual-networks/)
- [SpotTune: Transfer Learning Through Adaptive Fine-Tuning](https://scholariq.org/papers/spottune-transfer-learning-through-adaptive-fine-tuning/)
- [Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks](https://scholariq.org/papers/reflection-backdoor-a-natural-backdoor-attack-on-deep-neural-networks/)
- [Born Again Neural Networks](https://scholariq.org/papers/born-again-neural-networks/)
- [Generalized Autoencoder: A Neural Network Framework for Dimensionality Reduction](https://scholariq.org/papers/generalized-autoencoder-a-neural-network-framework-for-dimensionality-reduction/)
- [GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-efficient Medical Image Recognition](https://scholariq.org/papers/gloria-a-multimodal-global-local-representation-learning-framework-for-label/)
- [Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification](https://scholariq.org/papers/fully-adaptive-feature-sharing-in-multi-task-networks-with-applications-in/)
- [Dynamic Task Prioritization for Multitask Learning](https://scholariq.org/papers/dynamic-task-prioritization-for-multitask-learning/)
- [Exploiting local features from deep networks for image retrieval](https://scholariq.org/papers/exploiting-local-features-from-deep-networks-for-image-retrieval/)
- [Polarized self-attention: Towards high-quality pixel-wise mapping](https://scholariq.org/papers/polarized-self-attention-towards-high-quality-pixel-wise-mapping/)

## Topic primary papers

Showing 15 of 27.

- [One-shot learning of object categories](https://scholariq.org/papers/one-shot-learning-of-object-categories/)
- [A Decade Survey of Transfer Learning (2010–2020)](https://scholariq.org/papers/a-decade-survey-of-transfer-learning-2010-2020/)
- [A Comprehensive Survey of Image Augmentation Techniques for Deep Learning](https://scholariq.org/papers/a-comprehensive-survey-of-image-augmentation-techniques-for-deep-learning/)
- [Self-challenging Improves Cross-Domain Generalization](https://scholariq.org/papers/self-challenging-improves-cross-domain-generalization/)
- [SpotTune: Transfer Learning Through Adaptive Fine-Tuning](https://scholariq.org/papers/spottune-transfer-learning-through-adaptive-fine-tuning/)
- [Generalized Autoencoder: A Neural Network Framework for Dimensionality Reduction](https://scholariq.org/papers/generalized-autoencoder-a-neural-network-framework-for-dimensionality-reduction/)
- [Dynamic Task Prioritization for Multitask Learning](https://scholariq.org/papers/dynamic-task-prioritization-for-multitask-learning/)
- [Ridge Regression, Hubness, and Zero-Shot Learning](https://scholariq.org/papers/ridge-regression-hubness-and-zero-shot-learning/)
- [Domain Adaptation: Challenges, Methods, Datasets, and Applications](https://scholariq.org/papers/domain-adaptation-challenges-methods-datasets-and-applications/)
- [Deep Residual Correction Network for Partial Domain Adaptation](https://scholariq.org/papers/deep-residual-correction-network-for-partial-domain-adaptation/)
- [ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot](https://scholariq.org/papers/ace-ally-complementary-experts-for-solving-long-tailed-recognition-in-one-shot/)
- [PCL: Proxy-based Contrastive Learning for Domain Generalization](https://scholariq.org/papers/pcl-proxy-based-contrastive-learning-for-domain-generalization/)
- [Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning](https://scholariq.org/papers/learning-with-fantasy-semantic-aware-virtual-contrastive-constraint-for-few-shot/)
- [Distant Domain Transfer Learning for Medical Imaging](https://scholariq.org/papers/distant-domain-transfer-learning-for-medical-imaging/)
- [Data and domain knowledge dual‐driven artificial intelligence: Survey, applications, and challenges](https://scholariq.org/papers/data-and-domain-knowledge-dual-driven-artificial-intelligence-survey/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.