Technology Category
Machine Learning: Semi-Supervised LearningSearch and Optimization: Learning to SearchNatural Language Processing: Learning & Optimization for NLP
Unbiased Max-Min Embedding Classification for Transductive Few-Shot Learning: Clustering and Classification Are All You Need
🤖 AI Summary
This paper addresses three key challenges in transductive few-shot learning (TFSL): inter-class confusion, embedding distribution bias, and the hubness problem. To tackle these, we propose an unbiased embedding classification framework. Methodologically: (1) we introduce a decentered covariance modeling strategy to mitigate centroid bias; (2) we design an adaptive nonlinear embedding optimization that jointly enforces local alignment and global uniformity; and (3) we develop a variational Sinkhorn classifier that jointly optimizes prototype distances and transductive clustering. Evaluated on standard TFSL benchmarks, our approach significantly outperforms state-of-the-art methods. Results validate the effectiveness of the “clustering-as-classification” paradigm—achieving robust embedding learning and high-accuracy classification with only a minimal number of labeled examples. This work offers a novel perspective for few-shot learning under low-resource settings.
Technology Category
Application Category
📝 Abstract
Convolutional neural networks and supervised learning have achieved remarkable success in various fields but are limited by the need for large annotated datasets. Few-shot learning (FSL) addresses this limitation by enabling models to generalize from only a few labeled examples. Transductive few-shot learning (TFSL) enhances FSL by leveraging both labeled and unlabeled data, though it faces challenges like the hubness problem. To overcome these limitations, we propose the Unbiased Max-Min Embedding Classification (UMMEC) Method, which addresses the key challenges in few-shot learning through three innovative contributions. First, we introduce a decentralized covariance matrix to mitigate the hubness problem, ensuring a more uniform distribution of embeddings. Second, our method combines local alignment and global uniformity through adaptive weighting and nonlinear transformation, balancing intra-class clustering with inter-class separation. Third, we employ a Variational Sinkhorn Few-Shot Classifier to optimize the distances between samples and class prototypes, enhancing classification accuracy and robustness. These combined innovations allow the UMMEC method to achieve superior performance with minimal labeled data. Our UMMEC method significantly improves classification performance with minimal labeled data, advancing the state-of-the-art in TFSL.
Problem
Research questions and friction points this paper is trying to address.
Mitigates hubness problem in few-shot learning
Balances intra-class clustering and inter-class separation
Enhances classification accuracy with minimal labeled data
Innovation
Methods, ideas, or system contributions that make the work stand out.
Decentralized covariance matrix mitigates hubness problem
Adaptive weighting balances local and global alignment
Variational Sinkhorn classifier optimizes sample-prototype distances
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
No related jobs found.
