Search Results for author:
Found 4 papers, 3 papers with code
CL-MAE: Curriculum-Learned Masked Autoencoders
In this paper, we propose a curriculum learning approach that updates the masking strategy to continually increase the complexity of the self-supervised reconstruction task.
Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection
In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
Ranked #4 on
Anomaly Detection
on CUHK Avenue
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field.
Ranked #1 on
Anomaly Detection
on CUHK Avenue
(TBDC metric)
Information Nutrition Labels: A Plugin for Online News Evaluation
This paper discusses the choice of the labels, their implementation and visualization.
