Search Results for author:
Found 14 papers, 8 papers with code
Continuous diffusion for categorical data
Diffusion models have quickly become the go-to paradigm for generative modelling of perceptual signals (such as images and sound) through iterative refinement.
Self-conditioned Embedding Diffusion for Text Generation
Can continuous diffusion models bring the same performance breakthrough on natural language they did for image generation?
A Generalist Neural Algorithmic Learner
The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution.
Computer-Aided Design as Language
Computer-Aided Design (CAD) applications are used in manufacturing to model everything from coffee mugs to sports cars.
PolyGen: An Autoregressive Generative Model of 3D Meshes
Polygon meshes are an efficient representation of 3D geometry, and are of central importance in computer graphics, robotics and games development.
Unsupervised Doodling and Painting with Improved SPIRAL
We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804. 01118).
Synthesizing Programs for Images using Reinforced Adversarial Learning
Advances in deep generative networks have led to impressive results in recent years.
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models
Directed latent variable models that formulate the joint distribution as $p(x, z) = p(z) p(x \mid z)$ have the advantage of fast and exact sampling.
DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation
In this work, we consider the task of generating highly-realistic images of a given face with a redirected gaze.
Multiregion Bilinear Convolutional Neural Networks for Person Re-Identification
In this work we propose a new architecture for person re-identification.
Domain-Adversarial Training of Neural Networks
Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains.
Ranked #2 on
Domain Adaptation
on Synth Digits-to-SVHN
Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition
We propose a simple two-step approach for speeding up convolution layers within large convolutional neural networks based on tensor decomposition and discriminative fine-tuning.
Unsupervised Domain Adaptation by Backpropagation
Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled target-domain data is necessary).
Ranked #1 on
Domain Adaptation
on UCF-to-Olympic
$ N^4 $-Fields: Neural Network Nearest Neighbor Fields for Image Transforms
We propose a new architecture for difficult image processing operations, such as natural edge detection or thin object segmentation.
