Library to recognise and classify faces.
-
Updated
Jul 26, 2021 - Python
Library to recognise and classify faces.
A streamlit web app that allows you to train Few Shot image classification models
Comprehensive Study of Soft Prompting as a efficient method for Model Adaptation
A demonstration repo for how to do automatic translation using local llms.
LLMs for Low Resource Languages in Multilingual, Multimodal and Dialectal Settings
Code for "Improved Few-Shot Visual Classification"
Non-Euclidean implementations for few-shot image classification on the mini-ImageNet dataset
Automatic Categorization of Software Repository Domains with Minimal Resources
Using Few Shot Learning (FSL) for image classification on Oxford 17 Flowers dataset. Part of HKU COMP3340 Group 10 Project (2023-24 Sem2).
Code for paper the "Distance-Ratio-Based Formulation for Metric Learning"
Supplementary Material For the Paper "NUTS, NARS, and Speech"
Code Release of Exploring Sample Relationship for Few-Shot Classification
NAACL2022 Interactive Symbol Grounding with Complex Referential Expressions
This repository contains the source code for the IMAML-IDCG (ImageNet Model Agnostic Meta-learning for Invasive Ductal Carcinoma Grading)
Flower Recognition: Dealing with Less Data via 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
Official implementation of the paper: Learn to aggregate global and local representations for few-shot learning
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".
[TIP-2023] IEEE Trans.on Image Processing
Add a description, image, and links to the few-shot-classifcation topic page so that developers can more easily learn about it.
To associate your repository with the few-shot-classifcation topic, visit your repo's landing page and select "manage topics."