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 …
In this project, I explored deep similarity learning algorithms and their behaviour with different type of data (sequential data, spatial data, multimodal data). For each of these different modalities, I wrote 2 Medium articles detailing the retained method and providing my implementation.
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 …
These 3 projects are implementations made for the udacity's nanodegree program, all passed through a reviewer. They contain a small report, gathering my comprehension fo the algorithm as well as details on my implementation and my parameters.
In this project, I explored a Time Series of satellite images dataset by building different deep learning classifiers, finding inspiration in paper research in the field of Time Series classification.
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 …