A rule-based algorithm enabled automatic extraction of disease labels from tens of thousands of radiology reports. These weak labels were used to create deep learning models to classify multiple diseases for three different organ systems in body CT.

Weakly supervised street text detection, localisation and segmentation in Pytorch

Performed weakly supervised learning on CIFAR-10 images with noisy labels using convolutional neural networks (CNN).

Working Memory Inspired Hierarchical Video Decomposition with Transformative Representations

Simulation code for "Ordinal Multiple Instance Support Vector Machines"

Code accompanying the UQ AIML x MSS talk

PyTorch implementation of One-Shot Video Object Segmentation (OSVOS)

a collection of small machine learning projects

Learning threshold with weak supervision in Snorkel

Weak Supervised Fake News Detection with RoBERTa, XLNet, ALBERT, XGBoost and Logistic Regression classifiers.

An implementation for few computer vision models

Weakly Supervised Instance Segmentation using Class Peak Response, in CVPR 2018 (Spotlight)

mapping land cover at a national scale, using Sentinel-2 imagery and weak labels from CORINE land cover data

Experiments testing ordinal and multiple instance learning neural networks

Python Implementation of Weakly Supervised Clustering article

Dissertation for "Weakly Supervised Visual-Textual Grounding based on Concept Similarity" (MS thesis at University of Padua, Italy) - PyTorch implementation: https://github.com/lparolari/weakvtg

A database of over 500 published papers on Earth Observation with Remote Sensing data using Non-Supervised Deep Learning techniques, classified by their learning methods (Un-, Semi-, Self-, Transfer-, and Weakly-Supervised), Applications, Data, etc.

This repository hosts a public version of the code used for my bachelor thesis "Weakly supervised learning in RoboCup Scenarios".