116 citations · 212 across the 9 of their papers we have counts for
5 papers · 1 filter
Open-Ended Content-Style Recombination Via Leakage Filtering
Karl Ridgeway, Michael C. Mozer
We consider visual domains in which a class label specifies the content of an image, and class-irrelevant properties that differentiate instances constitute the style. We present a…
Adapted Deep Embeddings: A Synthesis of Methods for -Shot Inductive Transfer Learning
Tyler R. Scott, Karl Ridgeway, Michael C. Mozer
The focus in machine learning has branched beyond training classifiers on a single task to investigating how previously acquired knowledge in a source domain can be leveraged to fa…
Learning Deep Disentangled Embeddings with the F-Statistic Loss
Karl Ridgeway, Michael C. Mozer
Deep-embedding methods aim to discover representations of a domain that make explicit the domain's class structure and thereby support few-shot learning. Disentangling methods aim…
A Survey of Inductive Biases for Factorial Representation-Learning
Karl Ridgeway
With the resurgence of interest in neural networks, representation learning has re-emerged as a central focus in artificial intelligence. Representation learning refers to the disc…
Learning to Generate Images with Perceptual Similarity Metrics
Jake Snell, Karl Ridgeway, Renjie Liao +3
Deep networks are increasingly being applied to problems involving image synthesis, e.g., generating images from textual descriptions and reconstructing an input image from a compa…