57 citations · 93 across the 7 of their papers we have counts for
8 papers
A Compact and Semantic Latent Space for Disentangled and Controllable Image Editing
Gwilherm Lesné, Yann Gousseau, Saïd Ladjal +1
Recent advances in the field of generative models and in particular generative adversarial networks (GANs) have lead to substantial progress for controlled image editing, especiall…
An analysis of the transfer learning of convolutional neural networks for artistic images
Nicolas Gonthier, Yann Gousseau, Saïd Ladjal
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 a…
Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations
Antoine Pirovano, Hippolyte Heuberger, Sylvain Berlemont +2
Deep learning methods are widely used for medical applications to assist medical doctors in their daily routines. While performances reach expert's level, interpretability (highlig…
High resolution neural texture synthesis with long range constraints
Nicolas Gonthier, Yann Gousseau, Saïd Ladjal
The field of texture synthesis has witnessed important progresses over the last years, most notably through the use of Convolutional Neural Networks. However, neural synthesis meth…
PCAAE: Principal Component Analysis Autoencoder for organising the latent space of generative networks
Chi-Hieu Pham, Saïd Ladjal, Alasdair Newson
Autoencoders and generative models produce some of the most spectacular deep learning results to date. However, understanding and controlling the latent space of these models prese…
Regression Constraint for an Explainable Cervical Cancer Classifier
Antoine Pirovano, Leandro G. Almeida, Said Ladjal
This article adresses the problem of automatic squamous cells classification for cervical cancer screening using Deep Learning methods. We study different architectures on a public…
A PCA-like Autoencoder
Saïd Ladjal, Alasdair Newson, Chi-Hieu Pham
An autoencoder is a neural network which data projects to and from a lower dimensional latent space, where this data is easier to understand and model. The autoencoder consists of…
Weakly Supervised Object Detection in Artworks
Nicolas Gonthier, Yann Gousseau, Said Ladjal +1
We propose a method for the weakly supervised detection of objects in paintings. At training time, only image-level annotations are needed. This, combined with the efficiency of ou…