research
∙
09/15/2021
Resolution-robust Large Mask Inpainting with Fourier Convolutions
Modern image inpainting systems, despite the significant progress, often...
share
research
∙
06/15/2021
Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations
Averaging predictions over a set of models – an ensemble – is widely use...
research
∙
02/21/2020
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
Test-time data augmentation—averaging the predictions of a machine learn...
research
∙
02/15/2020
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
Uncertainty estimation and ensembling methods go hand-in-hand. Uncertain...
research
∙
05/01/2019
Semi-Conditional Normalizing Flows for Semi-Supervised Learning
This paper proposes a semi-conditional normalizing flow model for semi-s...
research
∙
10/16/2018
The Deep Weight Prior. Modeling a prior distribution for CNNs using generative models
Bayesian inference is known to provide a general framework for incorpora...
research
∙
03/10/2018
Variance Networks: When Expectation Does Not Meet Your Expectations
In this paper, we propose variance networks, a new model that stores the...
research
∙
02/20/2018
Bayesian Incremental Learning for Deep Neural Networks
In industrial machine learning pipelines, data often arrive in parts. Pa...
research
∙
02/13/2018
Uncertainty Estimation via Stochastic Batch Normalization
In this work, we investigate Batch Normalization technique and propose i...
research
∙
05/20/2017
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
Dropout-based regularization methods can be regarded as injecting random...
research
∙
01/19/2017
