One-Shot Learning
92 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find One-Shot Learning models and implementations
MNIST
TACO
TopLogo-10
MatSim
Latest papers
Learning Symbolic Task Representation from a Human-Led Demonstration: A Memory to Store, Retrieve, Consolidate, and Forget Experiences
We present a symbolic learning framework inspired by cognitive-like memory functionalities (i. e., storing, retrieving, consolidating and forgetting) to generate task representations to support high-level task planning and knowledge bootstrapping.
One Shot Learning as Instruction Data Prospector for Large Language Models
pldlgb/nuggets
•
•
Nuggets assesses the potential of individual instruction examples to act as effective one shot examples, thereby identifying those that can significantly enhance diverse task performance.
Towards One-Shot Learning for Text Classification using Inductive Logic Programming
With the ever-increasing potential of AI to perform personalised tasks, it is becoming essential to develop new machine learning techniques which are data-efficient and do not require hundreds or thousands of training data.
One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data
Brain extraction, registration and segmentation are indispensable preprocessing steps in neuroimaging studies.
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing
Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP).
UOD: Universal One-shot Detection of Anatomical Landmarks
heqin-zhu/uod_universal_oneshot_detection
•
•
However, existing one-shot learning methods are highly specialized in a single domain and suffer domain preference heavily in the situation of multi-domain unlabeled data.
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization
qgao21/corediff
•
•
First, CoreDiff utilizes LDCT images to displace the random Gaussian noise and employs a novel mean-preserving degradation operator to mimic the physical process of CT degradation, significantly reducing sampling steps thanks to the informative LDCT images as the starting point of the sampling process.
A Novel Embedding Architecture and Score Level Fusion Scheme for Occluded Image Acquisition in Ear Biometrics System
The reported performance metrics show the improvement achieved by using our proposed embedding network and fusing both sides of occluded ear images.
Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images
We propose a framework for the automatic one-shot segmentation of synthetic images generated by a StyleGAN.
Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles
sgottsch/tab2kg
•
•
We propose a one-shot learning approach that relies on these profiles to map a tabular dataset containing previously unseen instances to a domain ontology.
