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 with no code
Position and Orientation-Aware One-Shot Learning for Medical Action Recognition from Signal Data
Furthermore, the proposed privacy-preserved orientation-level features are utilized to assist the position-level features in both of the two stages for enhancing medical action recognition performance.
OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation
As deep learning methods continue to improve medical image segmentation performance, data annotation is still a big bottleneck due to the labor-intensive and time-consuming burden on medical experts, especially for 3D images.
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI
In this paper, we present a novel method to automatically classify medical images that learns and leverages weak causal signals in the image.
Bias Testing and Mitigation in LLM-based Code Generation
To mitigate bias for code generation models, we evaluate five bias mitigation prompt strategies, i. e., utilizing bias testing results to refine the code (zero-shot), one-, few-shot, and two Chain-of-Thought (CoT) prompts.
Harmonization Across Imaging Locations(HAIL): One-Shot Learning for Brain MRI
To prevent hallucination in medical imaging, such as magnetic resonance images (MRI) of the brain, we propose a one-shot learning method where we utilize neural style transfer for harmonization.
One-shot lip-based biometric authentication: extending behavioral features with authentication phrase information
LBBA can utilize both physical and behavioral characteristics of lip movements without requiring any additional sensory equipment apart from an RGB camera.
One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations
In this paper, we investigate the behavior of deep representations in widely used CNN models under extreme data scarcity for One-Shot periocular recognition, a biometric recognition task.
One-Shot Learning of Visual Path Navigation for Autonomous Vehicles
End-to-end deep learning models are comparatively simplistic models that can handle a broad set of scenarios.
Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis
We describe the approach and experiments that show how an agent, by retrieving and evaluating a breadth of responses from the LLM, can achieve 77-94% task completion in one-shot learning without user oversight.
One-shot Learning for Channel Estimation in Massive MIMO Systems
In this work, we propose a one-shot self-supervised learning framework for channel estimation in multi-input multi-output (MIMO) systems.
