baiksung/MeTAL
Official PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)
This project helps machine learning researchers and practitioners tackle 'few-shot learning' problems where you have very little labeled data for new tasks. It provides code to train models that can quickly adapt to new image classification tasks, even with just a few examples. The input is a dataset like miniImageNet, and the output is a trained model capable of classifying new images with high accuracy from limited samples.
No commits in the last 6 months.
Use this if you are a machine learning researcher working on meta-learning or few-shot image classification and need a robust, high-performance implementation.
Not ideal if you are a beginner looking for a simple, out-of-the-box solution for general image classification without diving into meta-learning specifics.
Stars
66
Forks
13
Language
Python
License
—
Category
Last pushed
Dec 18, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/baiksung/MeTAL"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
jakesnell/prototypical-networks
Code for the NeurIPS 2017 Paper "Prototypical Networks for Few-shot Learning"
harveyslash/Facial-Similarity-with-Siamese-Networks-in-Pytorch
Implementing Siamese networks with a contrastive loss for similarity learning
oscarknagg/few-shot
Repository for few-shot learning machine learning projects
google-research/meta-dataset
A dataset of datasets for learning to learn from few examples
Sha-Lab/FEAT
The code repository for "Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions"