Search Results for author:
Found 6 papers, 4 papers with code
Self-Supervised MultiModal Versatile Networks
In particular, we explore how best to combine the modalities, such that fine-grained representations of the visual and audio modalities can be maintained, whilst also integrating text into a common embedding.
Data-Efficient Image Recognition with Contrastive Predictive Coding
Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge.
Ranked #6 on
Contrastive Learning
on imagenet-1k
Hierarchical Autoregressive Image Models with Auxiliary Decoders
We show that autoregressive models conditioned on these representations can produce high-fidelity reconstructions of images, and that we can train autoregressive priors on these representations that produce samples with large-scale coherence.
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
We demonstrate the model's clinical applicability by assessing its performance on a test set of 21 CT scans from clinical practice, each with the 21 OARs segmented by two independent experts.
A Probabilistic U-Net for Segmentation of Ambiguous Images
To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses.
Exploiting Cyclic Symmetry in Convolutional Neural Networks
We evaluate the effect of these architectural modifications on three datasets which exhibit rotational symmetry and demonstrate improved performance with smaller models.
