Deep Learning

Beets or Cotton? Blind Extraction of Fine Agricultural Classes Using a Convolutional Autoencoder Applied to Temporal SAR Signatures

IEEE Transactions on Geoscience and Remote Sensing

Multi-branch Deep Learning model for detection of settlements without electricity

IGARSS 2021: International Geoscience and Remote Sensing Symposium

Convolutional Autoencoder for unsupervised representation learning of PolSAR Time-Series

IGARSS 2021: International Geoscience and Remote Sensing Symposium

MSc Thesis: Multimodal Similarity Learning for Duplicate Product Identification

State of the art models for Similarity Learning are all based on Deep Learning architecture using Siamese Network [Gregory et al., 2015]. They define a feature extraction pipeline that creates a latent representation of input data. This embedding …

How to choose your loss when designing a Siamese Neural Network ? Contrastive, Triplet or Quadruplet ?

Deep Similarity Learning is the training of a deep learning architecture to learn to detect similarity and disimilarity between two inputs (or more). In this article, I presented, studied and compared three of the most popular losses for the task of …

Introduction to Deep Similarity Learning for sequences

Deep Similarity Learning is the training of a deep learning architecture to learn to detect similarity and disimilarity between two inputs (or more). In this article, I focused on similarities between sentences, presenting the theory as well as …

Time Series Land Cover Challenge: a Deep Learning Perspective

Time Series Satellite Imagery is the addition of a temporal dimension to Satellite Imagery. We see in this post how a 10-bands satellite imagery takes benefit from this temporal dimension by using combination of unimodal and multimodel neural …