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.

Existing cross-domain recommendation methods rely on overlapping users and source interaction sequences to learn preference transfer function, leaving the rich structural information in pre-trained target-domain embeddings unexploited. In this paper, we propose COTA (Cluster Optimal Transport Alignment), a framework for cold-start CDR.

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.