A method that randomly mask out all features coming from a specific sensor in multi-sensor models for Earth observation. Depending on the fusion strategy, the mask out can be done at the input, feature or decision level.

The time-causal and time-recursive scale-space representation is obtained by filtering any 1-D signal with the time-causal limit kernel, and provides a way to define a multi-scale analysis for signals, for which the future cannot be accessed and additionally the computations should be strictly time-recursive, in order to not require any complementary memory of the past beyond the temporal scale-space representation itself.

time-caus-scsp

A time-causal and time-recursive scale-covariant scale-space representation of temporal signals and past time

Sensor Dropout

Self-supervised Vision Transformers for Joint SAR-optical Representation Learning

This study presents the analysis and principle of an innovative optimizer named weIghted meaN oF vectOrs (INFO) to optimize different problems. INFO is a modified weight mean method, whereby the weighted mean idea is employed for a solid structure and updating the vectors’ position using three core procedures: updating rule, vector combining, and a local search. The updating rule stage is based on a mean-based law and convergence acceleration to generate new vectors. The vector combining stage creates a combination of obtained vectors with the updating rule to achieve a promising solution. The updating rule and vector combining steps were improved in INFO to increase the exploration and exploitation capacities. Moreover, the local search stage helps this algorithm escape low-accuracy solutions and improve exploitation and convergence. The performance of INFO was evaluated in 48 mathematical test functions, and five constrained engineering test cases including optimal design of 10-reservoir system and 4-reservoir system. According to the literature, the results demonstrate that INFO outperforms other basic and advanced methods in terms of exploration and exploitation. In the case of engineering problems, the results indicate that the INFO can converge to 0.99% of the global optimum solution. Hence, the INFO algorithm is a promising tool for optimal designs in optimization problems, which stems from the considerable efficiency of this algorithm for optimizing constrained cases. The source codes of INFO algorithm are publicly available at 
https://aliasgharheidari.com/INFO.html

Source	Self-supervised Vision Transformers for Joint SAR-optical Representation Learning
Year
Data Source	Papers with Code archive (CC BY-SA)