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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–210 of 305 papers

TitleStatusHype
Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation—0
HalalNet: A Deep Neural Network that Classifies the Halalness Slaughtered Chicken from their Images—0
Learning Relational Representations by Analogy using Hierarchical Siamese Networks—0
Measuring Immediate Adaptation Performance for Neural Machine Translation—0
A Hippocampus Model for Online One-Shot Storage of Pattern Sequences—0
Image Deformation Meta-Networks for One-Shot LearningCode0
What Can ResNet Learn Efficiently, Going Beyond Kernels?—0
Few-Shot Adversarial Learning of Realistic Neural Talking Head ModelsCode0
One-Shot Learning for Text-to-SQL Generation—0
Variational Prototyping-Encoder: One-Shot Learning with Prototypical ImagesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified