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Found 4 papers, 2 papers with code
Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models
First, we introduce a scenario where the embedding of a pre-trained model is served through a gated API with compute-cost and data-privacy constraints.
Model-Agnostic Few-Shot Open-Set Recognition
Through extensive experiments spanning 5 datasets, we show that OSTIM surpasses both inductive and existing transductive methods in detecting open-set instances while competing with the strongest transductive methods in classifying closed-set instances.
Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling
Every day, a new method is published to tackle Few-Shot Image Classification, showing better and better performances on academic benchmarks.
MuCaLe-Net: Multi Categorical-Level Networks to Generate More Discriminating Features
In a transfer-learning scheme, the intermediate layers of a pre-trained CNN are employed as universal image representation to tackle many visual classification problems.
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