3.6k citations · 10k across the 28 of their papers we have counts for
8 papers · 1 filter
Do better ImageNet classifiers assess perceptual similarity better?
Manoj Kumar, Neil Houlsby, Nal Kalchbrenner +1
Perceptual distances between images, as measured in the space of pre-trained deep features, have outperformed prior low-level, pixel-based metrics on assessing perceptual similarit…
Colorization Transformer
Manoj Kumar, Dirk Weissenborn, Nal Kalchbrenner
We present the Colorization Transformer, a novel approach for diverse high fidelity image colorization based on self-attention. Given a grayscale image, the colorization proceeds i…
Axial Attention in Multidimensional Transformers
Jonathan Ho, Nal Kalchbrenner, Dirk Weissenborn +1
We propose Axial Transformers, a self-attention-based autoregressive model for images and other data organized as high dimensional tensors. Existing autoregressive models either su…
Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling
Jacob Menick, Nal Kalchbrenner
The unconditional generation of high fidelity images is a longstanding benchmark for testing the performance of image decoders. Autoregressive image models have been able to genera…
Parallel Multiscale Autoregressive Density Estimation
Scott Reed, Aäron van den Oord, Nal Kalchbrenner +4
PixelCNN achieves state-of-the-art results in density estimation for natural images. Although training is fast, inference is costly, requiring one network evaluation per pixel; O(N…
Video Pixel Networks
Nal Kalchbrenner, Aaron van den Oord, Karen Simonyan +4
We propose a probabilistic video model, the Video Pixel Network (VPN), that estimates the discrete joint distribution of the raw pixel values in a video. The model and the neural a…
Conditional Image Generation with PixelCNN Decoders
Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals +3
This work explores conditional image generation with a new image density model based on the PixelCNN architecture. The model can be conditioned on any vector, including descriptive…
Pixel Recurrent Neural Networks
Aaron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu
Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We…