# TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning

arXiv: https://arxiv.org/abs/1912.09033 · HTML: https://mlsanity.com/paper/1912.09033/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2019.

## Built on by (most cited first, 30 of 39)

- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Adaptive Consistency Regularization for Semi-Supervised Transfer Learning](https://mlsanity.com/paper/2103.02193/) (2021)
- [Matching Feature Sets for Few-Shot Image Classification](https://mlsanity.com/paper/2204.00949/) (2022)
- [APPLeNet: Visual Attention Parameterized Prompt Learning for Few-Shot Remote Sensing Image Generalization using CLIP](https://mlsanity.com/paper/2304.05995/) (2023)
- [Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning](https://mlsanity.com/paper/2109.05749/) (2021)
- [Dual Adaptive Representation Alignment for Cross-domain Few-shot Learning](https://mlsanity.com/paper/2306.10511/) (2023)
- [HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition](https://mlsanity.com/paper/2301.03330/) (2023)
- [Label Hallucination for Few-Shot Classification](https://mlsanity.com/paper/2112.03340/) (2021)
- [An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning](https://mlsanity.com/paper/2209.13777/) (2022)
- [SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch](https://mlsanity.com/paper/2212.00173/) (2022)
- [Multi-level Metric Learning for Few-shot Image Recognition](https://mlsanity.com/paper/2103.11383/) (2021)
- [Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison](https://mlsanity.com/paper/2203.14205/) (2022)
- [Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network](https://mlsanity.com/paper/2102.05176/) (2021)
- [Tensor feature hallucination for few-shot learning](https://mlsanity.com/paper/2106.05321/) (2021)
- [MHFC: Multi-Head Feature Collaboration for Few-Shot Learning](https://mlsanity.com/paper/2109.07785/) (2021)
- [Towards 3D Scene Understanding by Referring Synthetic Models](https://mlsanity.com/paper/2203.10546/) (2022)
- [MDFM: Multi-Decision Fusing Model for Few-Shot Learning](https://mlsanity.com/paper/2112.00690/) (2021)
- [Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction](https://mlsanity.com/paper/2112.12484/) (2021)
- [GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning](https://mlsanity.com/paper/2203.07738/) (2022)
- [Towards Few-Shot Learning in the Open World: A Review and Beyond](https://mlsanity.com/paper/2408.09722/) (2024)
- [Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation](https://mlsanity.com/paper/2510.08962/) (2025)
- [Local Propagation for Few-Shot Learning](https://mlsanity.com/paper/2101.01480/) (2021)
- [Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning](https://mlsanity.com/paper/2207.06817/) (2022)
- [Cross-Domain Few-Shot Classification via Inter-Source Stylization](https://mlsanity.com/paper/2208.08015/) (2022)
- [PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information](https://mlsanity.com/paper/2201.12928/) (2022)
- [Model2Scene: Learning 3D Scene Representation via Contrastive Language-CAD Models Pre-training](https://mlsanity.com/paper/2309.16956/) (2023)
- [Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning](https://mlsanity.com/paper/2403.18998/) (2024)
- [Few-Shot Learning in Video and 3D Object Detection: A Survey](https://mlsanity.com/paper/2507.17079/) (2025)
- [Causal Disentanglement and Cross-Modal Alignment for Enhanced Few-Shot Learning](https://mlsanity.com/paper/2508.03102/) (2025)
- [When Few-Shot Learning Meets Video Object Detection](https://mlsanity.com/paper/2103.14724/) (2021)

## Abstract (arXiv)

The successful application of deep learning to many visual recognition tasks relies heavily on the availability of a large amount of labeled data which is usually expensive to obtain. The few-shot learning problem has attracted increasing attention from researchers for building a robust model upon only a few labeled samples. Most existing works tackle this problem under the meta-learning framework by mimicking the few-shot learning task with an episodic training strategy. In this paper, we propose a new transfer-learning framework for semi-supervised few-shot learning to fully utilize the auxiliary information from labeled base-class data and unlabeled novel-class data. The framework consists of three components: 1) pre-training a feature extractor on base-class data; 2) using the feature extractor to initialize the classifier weights for the novel classes; and 3) further updating the model with a semi-supervised learning method. Under the proposed framework, we develop a novel method for semi-supervised few-shot learning called TransMatch by instantiating the three components with Imprinting and MixMatch. Extensive experiments on two popular benchmark datasets for few-shot learning, CUB-200-2011 and miniImageNet, demonstrate that our proposed method can effectively utilize the auxiliary information from labeled base-class data and unlabeled novel-class data to significantly improve the accuracy of few-shot learning task.