Search Results for author: Michal Drozdzal

Found 24 papers, 19 papers with code

On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction

1 code implementation30 Mar 2022 Tim Bakker, Matthew Muckley, Adriana Romero-Soriano, Michal Drozdzal, Luis Pineda

Most current approaches to undersampled multi-coil MRI reconstruction focus on learning the reconstruction model for a fixed, equidistant acquisition trajectory.

MRI Reconstruction SSIM

Parameter Prediction for Unseen Deep Architectures

1 code implementation NeurIPS 2021 Boris Knyazev, Michal Drozdzal, Graham W. Taylor, Adriana Romero-Soriano

We introduce a large-scale dataset of diverse computational graphs of neural architectures - DeepNets-1M - and use it to explore parameter prediction on CIFAR-10 and ImageNet.

Parameter Prediction

Active 3D Shape Reconstruction from Vision and Touch

1 code implementation NeurIPS 2021 Edward J. Smith, David Meger, Luis Pineda, Roberto Calandra, Jitendra Malik, Adriana Romero, Michal Drozdzal

In this paper, we focus on this problem and introduce a system composed of: 1) a haptic simulator leveraging high spatial resolution vision-based tactile sensors for active touching of 3D objects; 2)a mesh-based 3D shape reconstruction model that relies on tactile or visuotactile signals; and 3) a set of data-driven solutions with either tactile or visuotactile priors to guide the shape exploration.

3D Reconstruction 3D Shape Reconstruction

Generating unseen complex scenes: are we there yet?

no code implementations7 Dec 2020 Arantxa Casanova, Michal Drozdzal, Adriana Romero-Soriano

In this paper, we propose a methodology to compare complex scene conditional generation models, and provide an in-depth analysis that assesses the ability of each model to (1) fit the training distribution and hence perform well on seen conditionings, (2) to generalize to unseen conditionings composed of seen object combinations, and (3) generalize to unseen conditionings composed of unseen object combinations.

Instance Selection for GANs

1 code implementation NeurIPS 2020 Terrance DeVries, Michal Drozdzal, Graham W. Taylor

By refining the empirical data distribution before training, we redirect model capacity towards high-density regions, which ultimately improves sample fidelity, lowers model capacity requirements, and significantly reduces training time.

Conditional Image Generation

Active MR k-space Sampling with Reinforcement Learning

2 code implementations20 Jul 2020 Luis Pineda, Sumana Basu, Adriana Romero, Roberto Calandra, Michal Drozdzal

Deep learning approaches have recently shown great promise in accelerating magnetic resonance image (MRI) acquisition.

Image Reconstruction reinforcement-learning

3D Shape Reconstruction from Vision and Touch

1 code implementation NeurIPS 2020 Edward J. Smith, Roberto Calandra, Adriana Romero, Georgia Gkioxari, David Meger, Jitendra Malik, Michal Drozdzal

When a toddler is presented a new toy, their instinctual behaviour is to pick it upand inspect it with their hand and eyes in tandem, clearly searching over its surface to properly understand what they are playing with.

3D Shape Reconstruction

Extending Unsupervised Neural Image Compression With Supervised Multitask Learning

no code implementations MIDL 2019 David Tellez, Diederik Hoppener, Cornelis Verhoef, Dirk Grunhagen, Pieter Nierop, Michal Drozdzal, Jeroen van der Laak, Francesco Ciompi

Additionally, we trained multiple encoders with different training objectives, e. g. unsupervised and variants of MTL, and observed a positive correlation between the number of tasks in MTL and the system performance on the TUPAC16 dataset.

Image Compression

Needles in Haystacks: On Classifying Tiny Objects in Large Images

1 code implementation16 Aug 2019 Nick Pawlowski, Suvrat Bhooshan, Nicolas Ballas, Francesco Ciompi, Ben Glocker, Michal Drozdzal

In some important computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images.

General Classification Image Classification

On the Evaluation of Conditional GANs

1 code implementation11 Jul 2019 Terrance DeVries, Adriana Romero, Luis Pineda, Graham W. Taylor, Michal Drozdzal

We show that FJD can be used as a promising single metric for cGAN benchmarking and model selection.

Model Selection

Elucidating image-to-set prediction: An analysis of models, losses and datasets

1 code implementation11 Apr 2019 Luis Pineda, Amaia Salvador, Michal Drozdzal, Adriana Romero

In this paper, we identify an important reproducibility challenge in the image-to-set prediction literature that impedes proper comparisons among published methods, namely, researchers use different evaluation protocols to assess their contributions.

Multi-Label Classification

Inverse Cooking: Recipe Generation from Food Images

4 code implementations CVPR 2019 Amaia Salvador, Michal Drozdzal, Xavier Giro-i-Nieto, Adriana Romero

Our system predicts ingredients as sets by means of a novel architecture, modeling their dependencies without imposing any order, and then generates cooking instructions by attending to both image and its inferred ingredients simultaneously.

Recipe Generation

On the iterative refinement of densely connected representation levels for semantic segmentation

1 code implementation30 Apr 2018 Arantxa Casanova, Guillem Cucurull, Michal Drozdzal, Adriana Romero, Yoshua Bengio

State-of-the-art semantic segmentation approaches increase the receptive field of their models by using either a downsampling path composed of poolings/strided convolutions or successive dilated convolutions.

Scene Understanding Semantic Segmentation

Learnable Explicit Density for Continuous Latent Space and Variational Inference

no code implementations6 Oct 2017 Chin-wei Huang, Ahmed Touati, Laurent Dinh, Michal Drozdzal, Mohammad Havaei, Laurent Charlin, Aaron Courville

In this paper, we study two aspects of the variational autoencoder (VAE): the prior distribution over the latent variables and its corresponding posterior.

Density Estimation Variational Inference

Image Segmentation by Iterative Inference from Conditional Score Estimation

1 code implementation ICLR 2018 Adriana Romero, Michal Drozdzal, Akram Erraqabi, Simon Jégou, Yoshua Bengio

We experimentally find that the proposed iterative inference from conditional score estimation by conditional denoising autoencoders performs better than comparable models based on CRFs or those not using any explicit modeling of the conditional joint distribution of outputs.

Denoising Semantic Segmentation

Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation

no code implementations16 Feb 2017 Michal Drozdzal, Gabriel Chartrand, Eugene Vorontsov, Lisa Di Jorio, An Tang, Adriana Romero, Yoshua Bengio, Chris Pal, Samuel Kadoury

Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods.

Medical Image Segmentation Semantic Segmentation

The Importance of Skip Connections in Biomedical Image Segmentation

1 code implementation14 Aug 2016 Michal Drozdzal, Eugene Vorontsov, Gabriel Chartrand, Samuel Kadoury, Chris Pal

In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation.

Semantic Segmentation

Generic Feature Learning for Wireless Capsule Endoscopy Analysis

no code implementations26 Jul 2016 Santi Seguí, Michal Drozdzal, Guillem Pascual, Petia Radeva, Carolina Malagelada, Fernando Azpiroz, Jordi Vitrià

Most of the CAD systems in the capsule endoscopy share a common system design, but use very different image and video representations.

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