198 citations · 205 across the 8 of their papers we have counts for
12 papers · 1 filter
Is your noise correction noisy? PLS: Robustness to label noise with two stage detection
Paul Albert, Eric Arazo, Tarun Krishna +2
Designing robust algorithms capable of training accurate neural networks on uncurated datasets from the web has been the subject of much research as it reduces the need for time co…
Utilizing unsupervised learning to improve sward content prediction and herbage mass estimation
Paul Albert, Mohamed Saadeldin, Badri Narayanan +5
Sward species composition estimation is a tedious one. Herbage must be collected in the field, manually separated into components, dried and weighed to estimate species composition…
Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation
Paul Albert, Mohamed Saadeldin, Badri Narayanan +5
Herbage mass yield and composition estimation is an important tool for dairy farmers to ensure an adequate supply of high quality herbage for grazing and subsequently milk producti…
How Important is Importance Sampling for Deep Budgeted Training?
Eric Arazo, Diego Ortego, Paul Albert +2
Long iterative training processes for Deep Neural Networks (DNNs) are commonly required to achieve state-of-the-art performance in many computer vision tasks. Importance sampling a…
Semi-supervised dry herbage mass estimation using automatic data and synthetic images
Paul Albert, Mohamed Saadeldin, Badri Narayanan +5
Monitoring species-specific dry herbage biomass is an important aspect of pasture-based milk production systems. Being aware of the herbage biomass in the field enables farmers to…
Addressing out-of-distribution label noise in webly-labelled data
Paul Albert, Diego Ortego, Eric Arazo +2
A recurring focus of the deep learning community is towards reducing the labeling effort. Data gathering and annotation using a search engine is a simple alternative to generating…