MaryamHaghighat

FactoFormer

[IEEE T-GRS 2024] The official repository for Journal Article “FactoFormer: Factorized Hyperspectral Transformers with Self-Supervised Pre-Training”, Accepted to IEEE Transactions on Geoscience and Remote Sensing, December 2023.

ROPIM

[ICLR 2024 Spotlight] 🚀 The official repository of Self-Supervised Learning method "ROPIM", "Pre-training with Random Orthogonal Projection Image Modeling"

CLAM

Data Efficient & Weakly Supervised Computational Pathology on Whole Slide Images

fast-autoaugment

Official Implementation of 'Fast AutoAugment' in PyTorch.

Graph-Representation-Learning-Tutorial

Code for Data61's tutorial on Graph Representation Learning

multi_gpu_training

PathProfiler

Pipeline for computational pathology

renderHNE

Render pseudo HNE images from autofluorescence and DAPI signals

SimCLR

PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations

MaryamHaghighat/PathProfiler

Pipeline for computational pathology

MaryamHaghighat/CLAM

Data Efficient & Weakly Supervised Computational Pathology on Whole Slide Images

MaryamHaghighat/fast-autoaugment

Official Implementation of 'Fast AutoAugment' in PyTorch.

MaryamHaghighat/Graph-Representation-Learning-Tutorial

Code for Data61's tutorial on Graph Representation Learning

MaryamHaghighat/multi_gpu_training

MaryamHaghighat/renderHNE

Render pseudo HNE images from autofluorescence and DAPI signals

MaryamHaghighat/SimCLR

PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations

MaryamHaghighat/SimMIM

This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".

MaryamHaghighat/Swin-Transformer

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

MaryamHaghighat/unilm

Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities

MaryamHaghighat/VideoMAE

VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training