One-Shot Learning
93 papers with code • 1 benchmarks • 4 datasets
One-shot learning is the task of learning information about object categories from a single training example.
( Image credit: Siamese Neural Networks for One-shot Image Recognition )
Benchmarks
These leaderboards are used to track progress in One-Shot Learning
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Libraries
Use these libraries to find One-Shot Learning models and implementations
MNIST
TACO
TopLogo-10
MatSim
Latest papers
One-shot skill assessment in high-stakes domains with limited data via meta learning
yaniker/one-shot-skill-assessment-in-high-stakes-domains-with-limited-data-via-meta-learning
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This study marks the first instance of a domain-agnostic methodology for skill assessment in critical fields setting a precedent for the broad application of DL across diverse real-life domains with limited data.
One-shot recognition of any material anywhere using contrastive learning with physics-based rendering
The synthetic images were rendered using giant collections of textures, objects, and environments generated by computer graphics artists.
Learning New Tasks from a Few Examples with Soft-Label Prototypes
Existing approaches to few-shot learning in NLP rely on large language models and fine-tuning of these to generalise on out-of-distribution data.
A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationships
hubbs-lab-tamu/couple-stress-detection
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The proposed metric learning is based on a Siamese neural network (SNN) that learns the relative difference between pairs of samples from a target user and non-target users, thus being able to address the scarcity of labelled data from the target.
MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layers
Amotica/MergedNET
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Neurocomputing 2022
Recently, Siamese networks and similarity layers have been used to solve the one-shot learning problem, achieving state-of-the-art performance on visual-character recognition datasets.
BaseTransformers: Attention over base data-points for One Shot Learning
In this paper we propose to make use of the well-trained feature representations of the base dataset that are closest to each support instance to improve its representation during meta-test time.
One-shot Detail Retouching with Patch Space Neural Transformation Blending
faziletgokbudak/One-shot-Photo-Retouching
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Our method accurately transfers complex detail retouching edits.
Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification
A central challenge in training one-shot learning models is the limited representativeness of the available shots of the data space.
FETA: Towards Specializing Foundation Models for Expert Task Applications
alfassy/FETA
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However, as we show in this paper, FMs still have poor out-of-the-box performance on expert tasks (e. g. retrieval of car manuals technical illustrations from language queries), data for which is either unseen or belonging to a long-tail part of the data distribution of the huge datasets used for FM pre-training.
Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity
igitugraz/memorydependentcomputation
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Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years.
