14 citations · 21 across the 3 of their papers we have counts for
5 papers
Unsupervised Discriminative Learning of Sounds for Audio Event Classification
Sascha Hornauer, Ke Li, Stella X. Yu +2
Recent progress in network-based audio event classification has shown the benefit of pre-training models on visual data such as ImageNet. While this process allows knowledge transf…
Unsupervised deep learning for grading of age-related macular degeneration using retinal fundus images
Baladitya Yellapragada, Sascha Hornhauer, Kiersten Snyder +2
Many diseases are classified based on human-defined rubrics that are prone to bias. Supervised neural networks can automate the grading of retinal fundus images, but require labor-…
BatVision with GCC-PHAT Features for Better Sound to Vision Predictions
Jesper Haahr Christensen, Sascha Hornauer, Stella Yu
Inspired by sophisticated echolocation abilities found in nature, we train a generative adversarial network to predict plausible depth maps and grayscale layouts from sound. To ach…
BatVision: Learning to See 3D Spatial Layout with Two Ears
Jesper Haahr Christensen, Sascha Hornauer, Stella Yu
Many species have evolved advanced non-visual perception while artificial systems fall behind. Radar and ultrasound complement camera-based vision but they are often too costly and…
Fast Recurrent Fully Convolutional Networks for Direct Perception in Autonomous Driving
Yiqi Hou, Sascha Hornauer, Karl Zipser
Deep convolutional neural networks (CNNs) have been shown to perform extremely well at a variety of tasks including subtasks of autonomous driving such as image segmentation and ob…