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11/15/2021
Contrastive Representation Learning with Trainable Augmentation Channel
In contrastive representation learning, data representation is trained s...
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05/24/2019
Robustness to Adversarial Perturbations in Learning from Incomplete Data
What is the role of unlabeled data in an inference problem, when the pre...
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11/26/2018
Collaging on Internal Representations: An Intuitive Approach for Semantic Transfiguration
We present a novel CNN-based image editing method that allows the user t...
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05/16/2018
Neural Multi-scale Image Compression
This study presents a new lossy image compression method that utilizes t...
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02/16/2018
Spectral Normalization for Generative Adversarial Networks
One of the challenges in the study of generative adversarial networks is...
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02/15/2018
cGANs with Projection Discriminator
We propose a novel, projection based way to incorporate the conditional ...
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11/20/2017
Parameter Reference Loss for Unsupervised Domain Adaptation
The success of deep learning in computer vision is mainly attributed to ...
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05/31/2017
Spectral Norm Regularization for Improving the Generalizability of Deep Learning
We investigate the generalizability of deep learning based on the sensit...
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04/13/2017
Virtual Adversarial Training: a Regularization Method for Supervised and Semi-supervised Learning
We propose a new regularization method based on virtual adversarial loss...
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02/28/2017
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Learning discrete representations of data is a central machine learning ...
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05/25/2016
Adversarial Training Methods for Semi-Supervised Text Classification
Adversarial training provides a means of regularizing supervised learnin...
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07/02/2015
