3 citations · 3 across the 3 of their papers we have counts for
5 papers
Volumetric based mass flow estimation on sugarcane harvesters
Muhammad K. A. Hamdan, Diane T. Rover, Matthew J. Darr +1
Yield monitors on harvesters are a key component of precision agriculture. Mass flow estimation is the critical factor to measure, and having this allows for field productivity ana…
Generalizable semi-supervised learning method to estimate mass from sparsely annotated images
Muhammad K. A. Hamdan, Diane T. Rover, Matthew J. Darr +1
Mass flow estimation is of great importance to several industries, and it can be quite challenging to obtain accurate estimates due to limitation in expense or general infeasibilit…
Granular Learning with Deep Generative Models using Highly Contaminated Data
John Just
An approach to utilize recent advances in deep generative models for anomaly detection in a granular (continuous) sense on a real-world image dataset with quality issues is detaile…
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data
John Just, Sambuddha Ghosal
Advances in deep generative and density models have shown impressive capacity to model complex probability density functions in lower-dimensional space. Also, applying such models…
Mass Estimation from Images using Deep Neural Network and Sparse Ground Truth
Muhammad K A Hamdan, Daine T. Rover, Matthew J. Darr +1
Supervised learning is the workhorse for regression and classification tasks, but the standard approach presumes ground truth for every measurement. In real world applications, lim…