8 citations · 13 across the 6 of their papers we have counts for
6 papers
Silhouette Loss: Differentiable Global Structure Learning for Deep Representations
Matheus Vinícius Todescato, Joel Luís Carbonera
Learning discriminative representations is a central goal of supervised deep learning. While cross-entropy (CE) remains the dominant objective for classification, it does not expli…
No Labels Needed: Zero-Shot Image Classification with Collaborative Self-Learning
Matheus Vinícius Todescato, Joel Luís Carbonera
While deep learning, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), has significantly advanced classification performance, its typical reliance on e…
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification
Lucas M. Dorneles, Luan Fonseca Garcia, Joel Luís Carbonera
Neural networks have become increasingly popular in the last few years as an effective tool for the task of image classification due to the impressive performance they have achieve…
A Framework for testing Federated Learning algorithms using an edge-like environment
Felipe Machado Schwanck, Marcos Tomazzoli Leipnitz, Joel Luís Carbonera +1
Federated Learning (FL) is a machine learning paradigm in which many clients cooperatively train a single centralized model while keeping their data private and decentralized. FL i…
Accelerating prototype selection with spatial abstraction
Joel Luís Carbonera
The increasing digitalization in industry and society leads to a growing abundance of data available to be processed and exploited. However, the high volume of data requires consid…
An evaluation of pre-trained models for feature extraction in image classification
Erick da Silva Puls, Matheus V. Todescato, Joel L. Carbonera
In recent years, we have witnessed a considerable increase in performance in image classification tasks. This performance improvement is mainly due to the adoption of deep learning…