Search Results for author: Tiziano Portenier

Found 16 papers, 3 papers with code

Match What Matters: Generative Implicit Feature Replay for Continual Learning

no code implementations9 Jun 2021 Kevin Thandiackal, Tiziano Portenier, Andrea Giovannini, Maria Gabrani, Orcun Goksel

In particular, we significantly outperform the state-of-the-art in generative replay for various settings on the CIFAR-100 and CUB-200 datasets.

class-incremental learning Incremental Learning

Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps

no code implementations23 Feb 2021 Alvaro Gomariz, Tiziano Portenier, César Nombela-Arrieta, Orcun Goksel

We herein propose a deep learning-based cell detection framework that can operate on large microscopy images and outputs desired probabilistic predictions by (i) integrating Bayesian techniques for the regression of uncertainty-aware density maps, where peak detection can be applied to generate cell proposals, and (ii) learning a mapping from the numerous proposals to a probabilistic space that is calibrated, i. e. accurately represents the chances of a successful prediction.

Image Classification

Utilizing Uncertainty Estimation in Deep Learning Segmentation of Fluorescence Microscopy Images with Missing Markers

no code implementations27 Jan 2021 Alvaro Gomariz, Raphael Egli, Tiziano Portenier, César Nombela-Arrieta, Orcun Goksel

However, for combinations that do not exist in a labeled training dataset, one cannot have any estimation of potential segmentation quality if that combination is encountered during inference.

Semantic Segmentation

Learning Ultrasound Rendering from Cross-Sectional Model Slices for Simulated Training

no code implementations20 Jan 2021 Lin Zhang, Tiziano Portenier, Orcun Goksel

Given the high level of expertise required for navigation and interpretation of ultrasound images, computational simulations can facilitate the training of such skills in virtual reality.

Translation

GramGAN: Deep 3D Texture Synthesis From 2D Exemplars

no code implementations NeurIPS 2020 Tiziano Portenier, Siavash Bigdeli, Orcun Goksel

Inspired by recent advances in natural texture synthesis, we train deep neural models to generate textures by non-linearly combining learned noise frequencies.

Style Transfer Texture Synthesis

Deep Image Translation for Enhancing Simulated Ultrasound Images

no code implementations18 Jun 2020 Lin Zhang, Tiziano Portenier, Christoph Paulus, Orcun Goksel

To incorporate anatomical information potentially lost in low quality images, we additionally provide segmentation maps to image translation.

Image-to-Image Translation Translation

Learning Generative Models using Denoising Density Estimators

2 code implementations8 Jan 2020 Siavash A. Bigdeli, Geng Lin, Tiziano Portenier, L. Andrea Dunbar, Matthias Zwicker

Learning probabilistic models that can estimate the density of a given set of samples, and generate samples from that density, is one of the fundamental challenges in unsupervised machine learning.

Denoising Density Estimation

Smart, Deep Copy-Paste

no code implementations15 Mar 2019 Tiziano Portenier, Qiyang Hu, Paolo Favaro, Matthias Zwicker

In this work, we propose a novel system for smart copy-paste, enabling the synthesis of high-quality results given a masked source image content and a target image context as input.

Learning to Take Directions One Step at a Time

1 code implementation5 Dec 2018 Qiyang Hu, Adrian Wälchli, Tiziano Portenier, Matthias Zwicker, Paolo Favaro

Because items in an image can be animated in arbitrarily many different ways, we introduce as control signal a sequence of motion strokes.

Video Prediction

Understanding Degeneracies and Ambiguities in Attribute Transfer

no code implementations ECCV 2018 Attila Szabo, Qiyang Hu, Tiziano Portenier, Matthias Zwicker, Paolo Favaro

We study the problem of building models that can transfer selected attributes from one image to another without affecting the other attributes.

Specular-to-Diffuse Translation for Multi-View Reconstruction

no code implementations ECCV 2018 Shihao Wu, Hui Huang, Tiziano Portenier, Matan Sela, Danny Cohen-Or, Ron Kimmel, Matthias Zwicker

To alleviate this restriction, we introduce S2Dnet, a generative adversarial network for transferring multiple views of objects with specular reflection into diffuse ones, so that multi-view reconstruction methods can be applied more effectively.

3D Reconstruction Translation +1

FaceShop: Deep Sketch-based Face Image Editing

no code implementations24 Apr 2018 Tiziano Portenier, Qiyang Hu, Attila Szabó, Siavash Arjomand Bigdeli, Paolo Favaro, Matthias Zwicker

We present a novel system for sketch-based face image editing, enabling users to edit images intuitively by sketching a few strokes on a region of interest.

Image Manipulation

Disentangling Factors of Variation by Mixing Them

no code implementations CVPR 2018 Qiyang Hu, Attila Szabó, Tiziano Portenier, Matthias Zwicker, Paolo Favaro

We learn our representation without any labeling or knowledge of the data domain, using an autoencoder architecture with two novel training objectives: first, we propose an invariance objective to encourage that encoding of each attribute, and decoding of each chunk, are invariant to changes in other attributes and chunks, respectively; second, we include a classification objective, which ensures that each chunk corresponds to a consistently discernible attribute in the represented image, hence avoiding degenerate feature mappings where some chunks are completely ignored.

General Classification

Challenges in Disentangling Independent Factors of Variation

2 code implementations ICLR 2018 Attila Szabó, Qiyang Hu, Tiziano Portenier, Matthias Zwicker, Paolo Favaro

Such models could be used to encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis.

Image Generation

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