Papers › GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning

GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning

4 Sep 2020arXiv:2009.03665archive 2025-07-28

Lyes Khacef, Vincent Gripon, Benoit Miramond

Few-shot classification is a challenge in machine learning where the goal is to train a classifier using a very limited number of labeled examples. This scenario is likely to occur frequently in real life, for example when data acquisition or labeling is expensive. In this work, we consider the problem of post-labeled few-shot unsupervised learning, a classification task where representations are learned in an unsupervised fashion, to be later labeled using very few annotated examples. We argue that this problem is very likely to occur on the edge, when the embedded device directly acquires the data, and the expert needed to perform labeling cannot be prompted often. To address this problem, we consider an algorithm consisting of the concatenation of transfer learning with clustering using Self-Organizing Maps (SOMs). We introduce a TensorFlow-based implementation to speed-up the process in multi-core CPUs and GPUs. Finally, we demonstrate the effectiveness of the method using standard off-the-shelf few-shot classification benchmarks.

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lyes-khacef/GPU-SOM mentioned in papertfGPL-3.0 report

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Tasks

Classification Clustering Few-Shot Image Classification Few-Shot Learning General Classification Transfer Learning

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Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Few-Shot Image Classification	Mini-Imagenet 5-way (1-shot)	WRN + Self-Organizing Map	Accuracy	71.5	#32 of 105	Archive leaderboard	report
Few-Shot Image Classification	Mini-Imagenet 5-way (5-shot)	WRN + Self-Organizing Map	Accuracy	82.2	#42 of 95	Archive leaderboard	report

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