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Found 8 papers, 2 papers with code
Fourier Series Guided Design of Quantum Convolutional Neural Networks for Enhanced Time Series Forecasting
In this study, we apply 1D quantum convolution to address the task of time series forecasting.
A metric learning approach for endoscopic kidney stone identification
The proposed Guided Deep Metric Learning approach is based on a novel architecture which was designed to learn data representations in an improved way.
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification
Few-shot learning is a challenging area of research that aims to learn new concepts with only a few labeled samples of data.
CoShNet: A Hybrid Complex Valued Neural Network using Shearlets
In a hybrid neural network, the expensive convolutional layers are replaced by a non-trainable fixed transform with a great reduction in parameters.
MACFE: A Meta-learning and Causality Based Feature Engineering Framework
Feature engineering has become one of the most important steps to improve model prediction performance, and to produce quality datasets.
Guided Deep Metric Learning
Deep Metric Learning (DML) methods have been proven relevant for visual similarity learning.
On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification
Deep learning has shown great promise in diverse areas of computer vision, such as image classification, object detection and semantic segmentation, among many others.
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction
This module helps to improve the accuracy performance by allowing the similarity function, given by the metric learning method, to have more discriminative features for the classification.
