33 citations · 48 across the 3 of their papers we have counts for
7 papers · 1 filter
Learning Data Augmentation with Online Bilevel Optimization for Image Classification
Saypraseuth Mounsaveng, Issam Laradji, Ismail Ben Ayed +2
Data augmentation is a key practice in machine learning for improving generalization performance. However, finding the best data augmentation hyperparameters requires domain knowle…
Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation
Sai Rajeswar, Fahim Mannan, Florian Golemo +4
We infer and generate three-dimensional (3D) scene information from a single input image and without supervision. This problem is under-explored, with most prior work relying on su…
Slanted Stixels: A way to represent steep streets
Daniel Hernandez-Juarez, Lukas Schneider, Pau Cebrian +6
This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather re…
Adversarial Learning of General Transformations for Data Augmentation
Saypraseuth Mounsaveng, David Vazquez, Ismail Ben Ayed +1
Data augmentation (DA) is fundamental against overfitting in large convolutional neural networks, especially with a limited training dataset. In images, DA is usually based on heur…
Fourier-CPPNs for Image Synthesis
Mattie Tesfaldet, Xavier Snelgrove, David Vazquez
Compositional Pattern Producing Networks (CPPNs) are differentiable networks that independently map (x, y) pixel coordinates to (r, g, b) colour values. Recently, CPPNs have been u…
Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation
Lironne Kurzman, David Vazquez, Issam Laradji
We propose a Class-Based Styling method (CBS) that can map different styles for different object classes in real-time. CBS achieves real-time performance by carrying out two steps…