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Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation
Ke Wang, Hang Hua, Xiaojun Wan
Toaddresstheaboveproblems, wepropose amore flexible unsupervised textattributetransfer framework which replaces the process of modeling attribute with minimal editing of latent representations based on an attribute classifier.
A flexible model for training action localization with varying levels of supervision
Guilhem Chéron, Jean-Baptiste Alayrac, Ivan Laptev, Cordelia Schmid
Neural Information Processing Systems http://nips.cc/
Link Prediction Based on Graph Neural Networks
Neural Information Processing Systems http://nips.cc/
MetaInit: Initializing learning by learning to initialize
Yann N. Dauphin, Samuel Schoenholz
Neural Information Processing Systems http://nips.cc/
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
David Minnen, Johannes Ballé, George D. Toderici
Inspired by the success of autoregressive priors in probabilistic generative models,weexamineautoregressive,hierarchical, andcombined priorsasalternatives, weighing their costs and benefits in the context of image compression.
First, we derive a well-behaved regularized relativistic GAN loss that addresses issues of mode dropping and non-convergence that were previously tackled via a
There is a widely-spread claim that GANs are difficult to train, and GAN architectures in the literature are littered with empirical tricks.
