Shot Learning

Few-shot learning (FSL) aims to train machine learning models that can effectively learn new concepts or tasks from only a small number of examples, addressing the limitations of traditional methods requiring massive datasets. Current research focuses on improving model robustness to noisy data and heterogeneous tasks, exploring architectures like prototypical networks and meta-learning algorithms, and leveraging large vision-language models and external memory for enhanced performance. This field is crucial for advancing AI in data-scarce domains like medical image analysis and personalized medicine, where acquiring large labeled datasets is often impractical or impossible. The development of efficient and reliable FSL methods has significant implications for various applications, including object detection, natural language processing, and other areas where labeled data is limited.

Papers

November 18, 2024

Efficient Transfer Learning for Video-language Foundation Models
Haoxing Chen, Zizheng Huang, Yan Hong, Yanshuo Wang, Zhongcai Lyu, Zhuoer Xu, Jun Lan, Zhangxuan Gu
Vision Language Model Shot Learning Zero Shot Transfer Efficient Transfer Learning

November 15, 2024

On the Privacy Risk of In-context Learning
Haonan Duan, Adam Dziedzic, Mohammad Yaghini, Nicolas Papernot, Franziska Boenisch
Large Language Model Context Learning Shot Learning Membership Inference Attack Privacy Risk Natural Language Prompt

November 14, 2024

A Practical Guide to Fine-tuning Language Models with Limited Data
Márton Szép, Daniel Rueckert, Rüdiger von Eisenhart-Rothe, Florian Hinterwimmer
Natural Language Processing Transfer Learning Shot Learning Limited Data Practical Guide Language Model Fine Tuning

November 11, 2024

Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI
Bruno Viti, Franz Thaler, Kathrin Lisa Kapper, Martin Urschler, Martin Holler, Elias Karabelas
Gaussian Process Shot Learning Shot Segmentation Cardiac Magnetic Resonance Shot Approach

November 9, 2024

Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs
Shan Zhong, Jiahao Zeng, Yongxin Yu, Bohong Lin
Classification Code Semi Supervised Shot Learning Text Classification Text Classification Task Retrieval Augmented Text Generation

November 6, 2024

November 3, 2024

Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning
Fei Zhou, Peng Wang, Lei Zhang, Zhenghua Chen, Wei Wei, Chen Ding, Guosheng Lin, Yanning Zhang
LeArning Abstract Shot Learning Meta Learning Cross Domain Image to Image Frequency Prior

November 2, 2024

FEET: A Framework for Evaluating Embedding Techniques
Simon A. Lee, John Lee, Jeffrey N. Chiang
New Framework Representation Learning Foundation Model Shot Learning Jina Embeddings

November 1, 2024

October 28, 2024

Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models
Yilun Jin, Zheng Li, Chenwei Zhang, Tianyu Cao, Yifan Gao, Pratik Jayarao, Mao Li, Xin Liu, Ritesh Sarkhel, Xianfeng Tang, Haodong Wang, Zhengyang Wang, Wenju Xu, Jingfeng Yang, Qingyu Yin, Xian Li, Priyanka Nigam, Yi Xu, Kai Chen, Qiang Yang, Meng Jiang, Bing Yin
Language Model Shot Learning Multi Task Multi Task Benchmark Massive Multitask Language Understanding

October 27, 2024

Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin
Tianlin Guo, Lingling Zhang, Jiaxin Wang, Yuokuo Lei, Yifei Li, Haofen Wang, Jun Liu
Shot Learning Contrastive Loss Adaptive Importance Margin Maximization Shot Relation Extraction Adaptive Margin

October 24, 2024

Tailored-LLaMA: Optimizing Few-Shot Learning in Pruned LLaMA Models with Task-Specific Prompts
Danyal Aftab, Steven Davy
Large Language Model LeArning Abstract Shot Learning Task Specific Shot Classification LLaMA Model Fine Tuned Llama

October 22, 2024

October 21, 2024

Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification
Nishant S. Gaikwad, Lucas Heublein, Nisha L. Raichur, Tobias Feigl, Christopher Mutschler, Felix Ott
Shot Learning Global Navigation Satellite System Catastrophic Interference

October 14, 2024

Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework
Zhengwei Yang, Yuke Li, Qiang Sun, Basura Fernando, Heng Huang, Zheng Wang
LeArning Abstract Shot Learning Multi Modal Unimodal Model

October 10, 2024

DemoShapley: Valuation of Demonstrations for In-Context Learning
Shan Xie, Man Luo, Chadly Daniel Stern, Mengnan Du, Lu Cheng
Context Learning Shot Learning Noisy Demonstration Context Demonstration Private Valuation Demonstration Example

Shot Learning

Papers

Efficient Transfer Learning for Video-language Foundation Models

On the Privacy Risk of In-context Learning

A Practical Guide to Fine-tuning Language Models with Limited Data

Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI

Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs

Gradient Boosting Trees and Large Language Models for Tabular Data Few-Shot Learning

Quantum Diffusion Models for Few-Shot Learning

A Contrastive Self-Supervised Learning scheme for beat tracking amenable to few-shot learning

Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning

FEET: A Framework for Evaluating Embedding Techniques

Explainable few-shot learning workflow for detecting invasive and exotic tree species

Fast Adaptation with Kernel and Gradient based Meta Leaning

Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models

Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin

Tailored-LLaMA: Optimizing Few-Shot Learning in Pruned LLaMA Models with Task-Specific Prompts

Composing Diffusion Policies for Few-shot Learning of Movement Trajectories

Context-aware Prompt Tuning: Advancing In-Context Learning with Adversarial Methods

Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification

Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework

DemoShapley: Valuation of Demonstrations for In-Context Learning