We introduce a multi-branch Deep Learning architecture that allows for the extraction of multi-scale features. Exploiting the data multi-modality structure through the combined use of various feature extractors provides high performance on data …
Two oral presentations in IGARSS 2021:
- Convolutional Autoencoder for unsupervised representation learning of PolSAR Time-Series (5 min) (paper) - Multi-Branch Deep Learning model for detection of settlements without electricity (10 min) (paper) …
Temporal Convolutional AutoEncoders are used as feature extractors to project time series onto a latent space where similarity detection can be easily performed. This model can generate accurate descriptors of the temporal profile of the input …
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 …
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 …
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 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 …