SOTAVerified

One-Shot Learning

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 )

Papers

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Showing 61–70 of 305 papers

TitleStatusHype
Fast Adaptation in Generative Models with Generative Matching Networks—0
DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition—0
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI—0
Can Humans Do Less-Than-One-Shot Learning?—0
Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters—0
Brain-inspired Cognition in Next Generation Racetrack Memories—0
Bias Testing and Mitigation in LLM-based Code Generation—0
An Exploration of Three Lightly-supervised Representation Learning Approaches for Named Entity Classification—0
AFAT: Adaptive Failure-Aware Tracker for Robust Visual Object Tracking—0
Better Together: Resnet-50 accuracy with 13 fewer parameters and at 3 speed—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified