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A survey on text generation using generative adversarial networks
Gustavo Henrique de Rosa, João Paulo Papa
This work presents a thorough review concerning recent studies and text generation advancements using Generative Adversarial Networks. The usage of adversarial learning for text ge…
Video Segmentation Learning Using Cascade Residual Convolutional Neural Network
Daniel F. S. Santos, Rafael G. Pires, Danilo Colombo +1
Video segmentation consists of a frame-by-frame selection process of meaningful areas related to foreground moving objects. Some applications include traffic monitoring, human trac…
DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image Classification
Daniel F. S. Santos, Rafael G. Pires, Leandro A. Passos +1
Research on remote sensing image classification significantly impacts essential human routine tasks such as urban planning and agriculture. Nowadays, the rapid advance in technolog…
Improving Pre-Trained Weights Through Meta-Heuristics Fine-Tuning
Gustavo H. de Rosa, Mateus Roder, João Paulo Papa +1
Machine Learning algorithms have been extensively researched throughout the last decade, leading to unprecedented advances in a broad range of applications, such as image classific…
FEMa-FS: Finite Element Machines for Feature Selection
Lucas Biaggi, João P. Papa, Kelton A. P Costa +2
Identifying anomalies has become one of the primary strategies towards security and protection procedures in computer networks. In this context, machine learning-based methods emer…
PL-kNN: A Parameterless Nearest Neighbors Classifier
Danilo Samuel Jodas, Leandro Aparecido Passos, Ahsan Adeel +1
Demands for minimum parameter setup in machine learning models are desirable to avoid time-consuming optimization processes. The -Nearest Neighbors is one of the most effective…