7 citations · 9 across the 6 of their papers we have counts for
6 papers
Implicit Data Synthesis for Contrastive Unsupervised Data Augmentation
Patrick Kage, Trevor Hedges, N. Siddharth +1
Scientific observations generate large quantities of unlabeled data which is laborious to hand-label, making unsupervised learning techniques valuable for processing datasets. Amon…
Multi-modal, multi-scale representation learning for satellite imagery analysis just needs a good ALiBi
Patrick Kage, Pavlos Andreadis
Vision foundation models have been shown to be effective at processing satellite imagery into representations fit for downstream tasks, however, creating models which operate over…
A Review of Pseudo-Labeling for Computer Vision
Patrick Kage, Jay C. Rothenberger, Pavlos Andreadis +1
Deep neural models have achieved state of the art performance on a wide range of problems in computer science, especially in computer vision. However, deep neural networks often re…
Spherical Feature Pyramid Networks For Semantic Segmentation
Thomas Walker, Varun Anand, Pavlos Andreadis
Semantic segmentation for spherical data is a challenging problem in machine learning since conventional planar approaches require projecting the spherical image to the Euclidean p…
Class Introspection: A Novel Technique for Detecting Unlabeled Subclasses by Leveraging Classifier Explainability Methods
Patrick Kage, Pavlos Andreadis
Detecting latent structure within a dataset is a crucial step in performing analysis of a dataset. However, existing state-of-the-art techniques for subclass discovery are limited:…
Quantitative Analysis of Image Classification Techniques for Memory-Constrained Devices
Sebastian Müksch, Theo Olausson, John Wilhelm +1
Convolutional Neural Networks, or CNNs, are the state of the art for image classification, but typically come at the cost of a large memory footprint. This limits their usefulness…