14 citations · 41 across the 7 of their papers we have counts for
11 papers
Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders
Saeid Asgari Taghanaki, Mohammad Havaei, Alex Lamb +3
The latent variables learned by VAEs have seen considerable interest as an unsupervised way of extracting features, which can then be used for downstream tasks. There is a growing…
KaoKore: A Pre-modern Japanese Art Facial Expression Dataset
Yingtao Tian, Chikahiko Suzuki, Tarin Clanuwat +3
From classifying handwritten digits to generating strings of text, the datasets which have received long-time focus from the machine learning community vary greatly in their subjec…
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks
Alex Lamb, Sherjil Ozair, Vikas Verma +1
Deep networks have achieved excellent results in perceptual tasks, yet their ability to generalize to variations not seen during training has come under increasing scrutiny. In thi…
KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning
Tarin Clanuwat, Alex Lamb, Asanobu Kitamoto
Kuzushiji, a cursive writing style, had been used in Japan for over a thousand years starting from the 8th century. Over 3 millions books on a diverse array of topics, such as lite…
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Alex Lamb, Jonathan Binas, Anirudh Goyal +5
Machine learning promises methods that generalize well from finite labeled data. However, the brittleness of existing neural net approaches is revealed by notable failures, such as…
On Adversarial Mixup Resynthesis
Christopher Beckham, Sina Honari, Vikas Verma +5
In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders. We explore models that are capable of combining the a…