Search Results for author: Davide Zambrano

Found 8 papers, 3 papers with code

VIPriors 3: Visual Inductive Priors for Data-Efficient Deep Learning Challenges

no code implementations31 May 2023 Robert-Jan Bruintjes, Attila Lengyel, Marcos Baptista Rios, Osman Semih Kayhan, Davide Zambrano, Nergis Tomen, Jan van Gemert

The third edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" workshop featured four data-impaired challenges, focusing on addressing the limitations of data availability in training deep learning models for computer vision tasks.

Data Augmentation Representation Learning +1

DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations

4 code implementations17 Aug 2022 Gabriel Van Zandycke, Vladimir Somers, Maxime Istasse, Carlo Del Don, Davide Zambrano

With the recent development of Deep Learning applied to Computer Vision, sport video understanding has gained a lot of attention, providing much richer information for both sport consumers and leagues.

Camera Calibration Instance Segmentation +3

VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challenges

no code implementations21 Jan 2022 Attila Lengyel, Robert-Jan Bruintjes, Marcos Baptista Rios, Osman Semih Kayhan, Davide Zambrano, Nergis Tomen, Jan van Gemert

The second edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" challenges featured five data-impaired challenges, where models are trained from scratch on a reduced number of training samples for various key computer vision tasks.

Data Augmentation Transfer Learning

An image representation based convolutional network for DNA classification

1 code implementation ICLR 2018 Bojian Yin, Marleen Balvert, Davide Zambrano, Alexander Schönhuth, Sander Bohte

The folding structure of the DNA molecule combined with helper molecules, also referred to as the chromatin, is highly relevant for the functional properties of DNA.

Classification General Classification

Gating out sensory noise in a spike-based Long Short-Term Memory network

no code implementations ICLR 2018 Davide Zambrano, Isabella Pozzi, Roeland Nusselder, Sander Bohte

These adaptive spiking neurons implement an adaptive form of sigma-delta coding to convert internally computed analog activation values to spike-trains.

Efficient Computation in Adaptive Artificial Spiking Neural Networks

no code implementations13 Oct 2017 Davide Zambrano, Roeland Nusselder, H. Steven Scholte, Sander Bohte

Adaptive spike-time coding additionally allows for the dynamic control of neural coding precision: we show how a simple model of arousal in AdSNNs further halves the average required firing rate and this notion naturally extends to other forms of attention.

Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

no code implementations7 Sep 2016 Davide Zambrano, Sander M. Bohte

It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate.

Open-Ended Question Answering

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