Marco Pedersoli | DeepAI

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∙ 06/21/2023

Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture

Downsampling layers, including pooling and strided convolutions, are cru...

0 Mehraveh Javan, et al. ∙

∙ 06/01/2023

FigGen: Text to Scientific Figure Generation

The generative modeling landscape has experienced tremendous growth in r...

26 Juan A. Rodriguez, et al. ∙

∙ 10/19/2022

OCR-VQGAN: Taming Text-within-Image Generation

Synthetic image generation has recently experienced significant improvem...

19 Juan A. Rodriguez, et al. ∙

∙ 11/16/2021

Diversified Multi-prototype Representation for Semi-supervised Segmentation

This work considers semi-supervised segmentation as a dense prediction p...

0 Jizong Peng, et al. ∙

∙ 03/08/2021

Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization

The scarcity of labeled data often impedes the application of deep learn...

0 Jizong Peng, et al. ∙

∙ 06/25/2020

Learning Data Augmentation with Online Bilevel Optimization for Image Classification

Data augmentation is a key practice in machine learning for improving ge...

23 Saypraseuth Mounsaveng, et al. ∙

∙ 03/18/2020

Semi-supervised few-shot learning for medical image segmentation

Recent years have witnessed the great progress of deep neural networks o...

51 Abdur R Feyjie, et al. ∙

∙ 03/09/2020

On the Texture Bias for Few-Shot CNN Segmentation

Despite the initial belief that Convolutional Neural Networks (CNNs) are...

9 Reza Azad, et al. ∙

∙ 11/11/2019

A Computing Kernel for Network Binarization on PyTorch

Deep Neural Networks have now achieved state-of-the-art results in a wid...

0 Xianda Xu, et al. ∙

∙ 10/03/2019

Information based Deep Clustering: An experimental study

Recently, two methods have shown outstanding performance for clustering ...

0 Jizong Peng, et al. ∙

∙ 09/21/2019

Adversarial Learning of General Transformations for Data Augmentation

Data augmentation (DA) is fundamental against overfitting in large convo...

0 Saypraseuth Mounsaveng, et al. ∙

∙ 08/15/2019

Discretely-constrained deep network for weakly supervised segmentation

An efficient strategy for weakly-supervised segmentation is to impose co...

8 Jizong Peng, et al. ∙

∙ 07/06/2019

Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition

This paper presents a novel deep neural network (DNN) for multimodal fus...

0 Juan D. S. Ortega, et al. ∙

∙ 03/27/2019

Deep Co-Training for Semi-Supervised Image Segmentation

In this paper, we aim to improve the performance of semantic image segme...

0 Jizong Peng, et al. ∙

∙ 10/09/2018

Deep clustering: On the link between discriminative models and K-means

In the context of recent deep clustering studies, discriminative models ...

0 Mohammed Jabi, et al. ∙

∙ 07/09/2018

An Attention Model for group-level emotion recognition

In this paper we propose a new approach for classifying the global emoti...

0 Aarush Gupta, et al. ∙

∙ 11/29/2017

Modeling Information Flow Through Deep Neural Networks

This paper proposes a principled information theoretic analysis of class...

0 Ahmad Chaddad, et al. ∙

∙ 12/03/2016

Areas of Attention for Image Captioning

We propose "Areas of Attention", a novel attention-based model for autom...

0 Marco Pedersoli, et al. ∙

∙ 06/15/2016

DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers

In this paper, a new method for generating object and action proposals i...

0 Amir Ghodrati, et al. ∙

∙ 10/15/2015

DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers

In this paper we evaluate the quality of the activation layers of a conv...

0 Amir Ghodrati, et al. ∙

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