10 citations · 24 across the 11 of their papers we have counts for
13 papers
An accurate detection is not all you need to combat label noise in web-noisy datasets
Paul Albert, Jack Valmadre, Eric Arazo +3
Training a classifier on web-crawled data demands learning algorithms that are robust to annotation errors and irrelevant examples. This paper builds upon the recent empirical obse…
Dataset Clustering for Improved Offline Policy Learning
Qiang Wang, Yixin Deng, Francisco Roldan Sanchez +4
Offline policy learning aims to discover decision-making policies from previously-collected datasets without additional online interactions with the environment. As the training da…
Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiency
Francisco Roldan Sanchez, Qiang Wang, David Cordova Bulens +3
Hindsight Experience Replay (HER) is a technique used in reinforcement learning (RL) that has proven to be very efficient for training off-policy RL-based agents to solve goal-base…
Enhancing CLIP with GPT-4: Harnessing Visual Descriptions as Prompts
Mayug Maniparambil, Chris Vorster, Derek Molloy +3
Contrastive pretrained large Vision-Language Models (VLMs) like CLIP have revolutionized visual representation learning by providing good performance on downstream datasets. VLMs a…
Self-Supervised and Semi-Supervised Polyp Segmentation using Synthetic Data
Enric Moreu, Eric Arazo, Kevin McGuinness +1
Early detection of colorectal polyps is of utmost importance for their treatment and for colorectal cancer prevention. Computer vision techniques have the potential to aid professi…
Joint one-sided synthetic unpaired image translation and segmentation for colorectal cancer prevention
Enric Moreu, Eric Arazo, Kevin McGuinness +1
Deep learning has shown excellent performance in analysing medical images. However, datasets are difficult to obtain due privacy issues, standardization problems, and lack of annot…