The proposed approach is more inline with the artificial general intelligence paradigm as the neural network learns the scene where it is deployed without any supervision (target labels and tasks) and without forgetting about the past.
The best performing image-based self-supervised representation learning method is then used for video anomaly detection to see the importance of spatial features in visual anomaly detection in videos.
Anticipation of accidents ahead of time in autonomous and non-autonomous vehicles aids in accident avoidance.
Our method offers a good trade-off between the number of parameters and classification accuracy.
Ranked #1 on Image Classification on Fashion-MNIST
With a single epoch of training, our method improves the AUC by 8. 03% compared to the convolutional LSTM-based approach.
The better accuracy and complexity compromise, as well as the extremely fast speed of our method makes it suitable for neural network compression.