There are 2 repositories under few-shot-classifcation topic.
Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification.
[ICLR2021 Oral] Free Lunch for Few-Shot Learning: Distribution Calibration
Leaderboards for few-shot image classification on miniImageNet, tieredImageNet, FC100, and CIFAR-FS.
This repository contains an easy and intuitive approach to few-shot NER using most similar expansion over spaCy embeddings. Now with entity scoring.
This repository contains an easy and intuitive approach to few-shot classification using sentence-transformers or spaCy models, or zero-shot classification with Huggingface.
[ICCV 2023] Prompt-aligned Gradient for Prompt Tuning
Source codes for "Improved Few-Shot Visual Classification" (CVPR 2020), "Enhancing Few-Shot Image Classification with Unlabelled Examples" (WACV 2022), and "Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning" (Neural Networks 2022 - in submission)
[ICCV'21] Official PyTorch implementation of Relational Embedding for Few-Shot Classification
A non-official 100% PyTorch implementation of META-DATASET benchmark for few-shot classification
[ICML 2022] Channel Importance Matters in Few-shot Image Classification
Source codes for "Improved Few-Shot Visual Classification" (CVPR 2020), "Enhancing Few-Shot Image Classification with Unlabelled Examples" (WACV 2022), and "Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning" (Neural Networks 2022 - in submission)
Code release for Proto-CLIP: Vision-Language Prototypical Network for Few-Shot Learning
Few-Shot Graph Classification via distance metric learning.
Official Implementation of CVPR 2023 paper: "Meta-Learning with a Geometry-Adaptive Preconditioner"
This repository contains the experiments conducted in the ICLR 2022 spotlight paper "On the Importance of Firth Bias Reduction in Few-Shot Classification".
[CVPR-2022] ''Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches'', IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2022.
Implementation of Few-shot Binary Image Classification using Contrastive Learning-based Approach in PyTorch
Code Repository for "SSL-ProtoNet: Self-supervised Learning Prototypical Networks for few-shot learning"
Parkinson detection based on wave sketches and deep learning - PDS
Something-something-v2 video dataset is splitted into 3 meta-sets, namely, meta-training, meta-validation, meta-test. Overall, dataset includes 100 classes that are divided according to CMU [1] The code also provides a dataloader in order to create episodes considering given n-way k-shot learning task. Videos are converted to the frames under sparse-sampling protocol described in TSN [2]
Project for Deep Learning And Applied AI course at the University of "La Sapienza" in Master in Computer Science A.A. 2021/2022
This repository contains the main ResNet backbone experiments conducted in the ICLR 2022 spotlight paper "On the Importance of Firth Bias Reduction in Few-Shot Classification".
The code for "SCL: Self-supervised contrastive learning for few-shot image classification"
GUI based tool to train and develop Few Shot Classification ML model.
The goal of this project is to improve data augmentation by incorporating a diffusion model, with a special focus on enriching semantic diversity. The Few-Shot Classification Performance is used as a metric for evaluating the implemented improvements.
A slim implementation of Self-Pooling Transformer for hyperspectral image classification.
[TIP-2023] IEEE Trans.on Image Processing
Official implementation of the paper: Learn to aggregate global and local representations for few-shot learning
This repository contains the firth bias reduction experiments on the few-shot distribution calibration method conducted in the ICLR 2022 spotlight paper "On the Importance of Firth Bias Reduction in Few-Shot Classification".
Mineral Prediction based on Prototype Learning