Deep Image Prior
In this paper we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting.
It Takes (Only) Two: Adversarial Generator-Encoder Networks
AAAI 2018 (oral)
We present a new autoencoder-type architecture, that is trainable in an unsupervised mode, sustains both generation and inference, and has the quality of conditional and unconditional samples boosted by adversarial learning.
Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis
We introduce Instance Normalization for a better stylization and derive entropy loss which improves samples diversity for both texture synthesis and stylization.
Neural Texture Synthesis and Style Transfer for Audio
We present an extension of texture synthesis and style transfer method of Leon Gatys et al. for audio. Joint work with Vadim Lebedev.
A multicore modification of L. Van der Maaten's Barnes-Hut t-SNE with python and Torch CFFI-based wrappers. The code also works faster than sklearn.TSNE on 1 core.
Online Neural Doodle
"Fast neural doodle" + "Texture Nets" = "Online neural doodle". Feed-forward generator allows real-time applications so we've built a web demo likeMonet.
Fast Neural Doodle
Neural doodle using gram matrices matching as opposed to original patch-based method.
Large Image Viewer
Web-based viewer for very large images.
Pytorch in Theano
Run Pytorch graphs inside any Theano graph.