Code release for Proto-CLIP: Vision-Language Prototypical Network for Few-Shot Learning | IROS 2024
A Lightweight and High Performance Neural network for MI-EEG decoding
Case-based interpretable deep learning for ECG classification. This code implements ProtoECGNet from the following paper: "ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification." Sethi et al., MLHC 2025
Official Pytorch Code of Our Paper: Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Need
Hyperbolic Busemann Learning with Ideal Prototypes, NeurIPS2021
[WACV 2025] LatteCLIP: Unsupervised CLIP Fine-Tuning via LMM-Synthetic Texts
[MM2024] PyTorch implementation of "Semantic Codebook Learning for Dynamic Recommendation Models".
Case-based interpretable deep learning for ECG classification. This code implements ProtoECGNet from the following paper: "ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification." Sethi et al., MLHC 2025
Source code for the ADAM submission on the PlantCLEF2025 challenge
A lightweight Few-Shot Image Classifier built with PyTorch, capable of recognizing new classes from just a few examples using prototype-based learning. Designed for easy model export and integration.
Frozen encoder + Mahalanobis prototype for class-incremental intent classification. 50+ experiments across BANKING77, CLINC150, HWU64, AG News. Matches fine-tuned baselines at 5MB state with zero forgetting, order-invariance, 455 QPS.
Add a description, image, and links to the prototype-learning topic page so that developers can more easily learn about it.
To associate your repository with the prototype-learning topic, visit your repo's landing page and select "manage topics."