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Found 6 papers, 0 papers with code
Frugal Satellite Image Change Detection with Deep-Net Inversion
The main contribution resides in a novel adversarial model that allows learning the most representative, diverse and uncertain virtual exemplars (as inverted preimages of the trained DNNs) that challenge (the most) the trained DNNs, and this leads to a better re-estimate of these networks in the subsequent iterations of active learning.
Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection
Our method is interactive and relies on a question and answer model which asks the oracle (user) questions about the most informative display (dubbed as virtual exemplars), and according to the user's responses, updates change detections.
Frugal Reinforcement-based Active Learning
The proposed approach is probabilistic and unifies all these criteria in a single objective function whose solution models the probability of relevance of samples (i. e., how critical) when learning a decision function.
Reinforcement-based frugal learning for satellite image change detection
To further explore the potential of this objective function, we consider a reinforcement learning approach that finds the best combination of diversity, representativity and uncertainty, through active learning iterations, leading to better generalization as corroborated through experiments in interactive satellite image change detection.
Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detection
In this paper, we devise a novel interactive satellite image change detection algorithm based on active learning.
Active learning for interactive satellite image change detection
We introduce in this paper a novel active learning algorithm for satellite image change detection.
