Search Results for author:
Found 9 papers, 5 papers with code
Hybrid Training of Denoising Networks to Improve the Texture Acutance of Digital Cameras
In order to evaluate the capacity of a camera to render textures properly, the standard practice, used by classical scoring protocols, is to compute the frequential response to a dead leaves image target, from which is built a texture acutance metric.
A Compact and Semantic Latent Space for Disentangled and Controllable Image Editing
Recent advances in the field of generative models and in particular generative adversarial networks (GANs) have lead to substantial progress for controlled image editing, especially compared with the pre-deep learning era.
An analysis of the transfer learning of convolutional neural networks for artistic images
Transfer learning from huge natural image datasets, fine-tuning of deep neural networks and the use of the corresponding pre-trained networks have become de facto the core of art analysis applications.
Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations
We formalize the design of WSI classification architectures and propose a piece-wise interpretability approach, relying on gradient-based methods, feature visualization and multiple instance learning context.
High resolution neural texture synthesis with long range constraints
Experiments show the interest of the multi-scale scheme for high resolution textures and the interest of combining it with additional constraints for regular textures.
Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts
Weakly supervised object detection (WSOD) using only image-level annotations has attracted a growing attention over the past few years.
Ranked #1 on
Weakly Supervised Object Detection
on CASPAPaintings
PCAAE: Principal Component Analysis Autoencoder for organising the latent space of generative networks
We hope that this approach will contribute to better understanding of the intrinsic latent spaces of powerful deep generative models.
Processsing Simple Geometric Attributes with Autoencoders
This results in a wide range of practical problems, such as difficulties in training, the tendency to sample images with little or no variability, and generalisation problems.
