Gemma Roig | DeepAI

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∙ 08/20/2022

Net2Brain: A Toolbox to compare artificial vision models with human brain responses

We introduce Net2Brain, a graphical and command-line user interface tool...

0 Domenic Bersch, et al. ∙

∙ 08/09/2022

Using Sentence Embeddings and Semantic Similarity for Seeking Consensus when Assessing Trustworthy AI

Assessing the trustworthiness of artificial intelligence systems require...

0 Dennis Vetter, et al. ∙

∙ 06/17/2022

What do navigation agents learn about their environment?

Today's state of the art visual navigation agents typically consist of l...

0 Kshitij Dwivedi, et al. ∙

∙ 12/28/2021

FRIDA – Generative Feature Replay for Incremental Domain Adaptation

We tackle the novel problem of incremental unsupervised domain adaptatio...

20 Sayan Rakshit, et al. ∙

∙ 10/21/2020

AttendAffectNet: Self-Attention based Networks for Predicting Affective Responses from Movies

In this work, we propose different variants of the self-attention based ...

0 Ha Thi Phuong Thao, et al. ∙

∙ 08/05/2020

Duality Diagram Similarity: a generic framework for initialization selection in task transfer learning

In this paper, we tackle an open research question in transfer learning,...

4 Kshitij Dwivedi, et al. ∙

∙ 06/30/2020

Using Human Psychophysics to Evaluate Generalization in Scene Text Recognition Models

Scene text recognition models have advanced greatly in recent years. Ins...

24 Sahar Siddiqui, et al. ∙

∙ 01/25/2020

Regression-based music emotion prediction using triplet neural networks

In this paper, we adapt triplet neural networks (TNNs) to a regression t...

0 Kin Wai Cheuk, et al. ∙

∙ 10/22/2019

Predictive Coding Networks Meet Action Recognition

Action recognition is a key problem in computer vision that labels video...

19 Xia Huang, et al. ∙

∙ 10/01/2019

Latent space representation for multi-target speaker detection and identification with a sparse dataset using Triplet neural networks

We present an approach to tackle the speaker recognition problem using T...

0 Kin Wai Cheuk, et al. ∙

∙ 09/16/2019

Multimodal Deep Models for Predicting Affective Responses Evoked by Movies

The goal of this study is to develop and analyze multimodal models for p...

0 Ha Thi Phuong Thao, et al. ∙

∙ 05/14/2019

The Algonauts Project: A Platform for Communication between the Sciences of Biological and Artificial Intelligence

In the last decade, artificial intelligence (AI) models inspired by the ...

1 Radoslaw Martin Cichy, et al. ∙

∙ 04/26/2019

Representation Similarity Analysis for Efficient Task taxonomy & Transfer Learning

Transfer learning is widely used in deep neural network models when ther...

20 Kshitij Dwivedi, et al. ∙

∙ 04/22/2019

Deep Anchored Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have been proven to be extremely su...

0 Jiahui Huang, et al. ∙

∙ 04/01/2019

JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields

Deep learning techniques have become the to-go models for most vision-re...

0 Quang-Hieu Pham, et al. ∙

∙ 08/23/2018

DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN

Recently, the introduction of the generative adversarial network (GAN) a...

16 Swee Kiat Lim, et al. ∙

∙ 06/26/2017

Do Deep Neural Networks Suffer from Crowding?

Crowding is a visual effect suffered by humans, in which an object that ...

0 Anna Volokitin, et al. ∙

∙ 11/14/2016

Herding Generalizes Diverse M -Best Solutions

We show that the algorithm to extract diverse M -solutions from a Condit...

0 Ece Ozkan, et al. ∙

∙ 08/29/2014

Comment on "Ensemble Projection for Semi-supervised Image Classification"

In a series of papers by Dai and colleagues [1,2], a feature map (or ker...

0 Xavier Boix, et al. ∙

∙ 09/16/2013

SEEDS: Superpixels Extracted via Energy-Driven Sampling

Superpixel algorithms aim to over-segment the image by grouping pixels t...

0 Michael Van den Bergh, et al. ∙

∙ 07/19/2013

Random Binary Mappings for Kernel Learning and Efficient SVM

Support Vector Machines (SVMs) are powerful learners that have led to st...

0 Gemma Roig, et al. ∙

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