3.2k citations · 11.3k across the 87 of their papers we have counts for
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Semantically Interpretable Activation Maps: what-where-how explanations within CNNs
Diego Marcos, Sylvain Lobry, Devis Tuia
A main issue preventing the use of Convolutional Neural Networks (CNN) in end user applications is the low level of transparency in the decision process. Previous work on CNN inter…
Adaptive Compression-based Lifelong Learning
Shivangi Srivastava, Maxim Berman, Matthew B. Blaschko +1
The problem of a deep learning model losing performance on a previously learned task when fine-tuned to a new one is a phenomenon known as Catastrophic forgetting. There are two ma…
Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning
Benjamin Kellenberger, Diego Marcos, Sylvain Lobry +1
We present an Active Learning (AL) strategy for re-using a deep Convolutional Neural Network (CNN)-based object detector on a new dataset. This is of particular interest for wildli…
Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution
Shivangi Srivastava, John E. Vargas-Muñoz, Devis Tuia
Landuse characterization is important for urban planning. It is traditionally performed with field surveys or manual photo interpretation, two practices that are time-consuming and…
Wasserstein Adversarial Regularization (WAR) on label noise
Kilian Fatras, Bharath Bhushan Damodaran, Sylvain Lobry +3
Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning ro…
Correcting rural building annotations in OpenStreetMap using convolutional neural networks
John E. Vargas-Muñoz, Sylvain Lobry, Alexandre X. Falcão +1
Rural building mapping is paramount to support demographic studies and plan actions in response to crisis that affect those areas. Rural building annotations exist in OpenStreetMap…