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
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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
Exploring the potential of prototype-based soft-labels data distillation for imbalanced data classification
Dataset distillation aims at synthesizing a dataset by a small number of artificially generated data items, which, when used as training data, reproduce or approximate a machine learning (ML) model as if it were trained on the entire original dataset.
A Feature-based Generalizable Prediction Model for Both Perceptual and Abstract Reasoning
We applied our model to a simplified Raven's Progressive Matrices task, previously designed for behavioral testing and neuroimaging in humans.
Leveraging Weakly Annotated Data for Hate Speech Detection in Code-Mixed Hinglish: A Feasibility-Driven Transfer Learning Approach with Large Language Models
Zero-shot learning, one-shot learning, and few-shot learning and prompting approaches have then been applied to assign labels to the comments and compare them to human-assigned labels.
More than the Sum of Its Parts: Ensembling Backbone Networks for Few-Shot Segmentation
\acrlong{fss}, in particular, concerns the extension and optimization of traditional segmentation methods in challenging conditions where limited training examples are available.
AHAM: Adapt, Help, Ask, Model -- Harvesting LLMs for literature mining
Our system aims to reduce both the ratio of outlier topics to the total number of topics and the similarity between topic definitions.
Prototype-Based Approach for One-Shot Segmentation of Brain Tumors using Few-Shot Learning
In order to distinguish the query images from the class prototypes, we employ a metric learning-based approach that relies on non-parametric thresholds.
Little Giants: Exploring the Potential of Small LLMs as Evaluation Metrics in Summarization in the Eval4NLP 2023 Shared Task
This paper describes and analyzes our participation in the 2023 Eval4NLP shared task, which focuses on assessing the effectiveness of prompt-based techniques to empower Large Language Models to handle the task of quality estimation, particularly in the context of evaluating machine translations and summaries.
SparseDFF: Sparse-View Feature Distillation for One-Shot Dexterous Manipulation
Central to SparseDFF is a feature refinement network, optimized with a contrastive loss between views and a point-pruning mechanism for feature continuity.
An Event based Prediction Suffix Tree
This article introduces the Event based Prediction Suffix Tree (EPST), a biologically inspired, event-based prediction algorithm.
Temporal credit assignment for one-shot learning utilizing a phase transition material
Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient artificial intelligence and learning machines.
