Abstract

Sparse secret writing, primarily based on abnormal detection, has shown promising performance, key features being feature learning, subtle illustrations, and vocabulary learning. propose a replacement neural network for anomaly detection called AnomalyNet by deep feature learning, sparse representation, and dictionary learning in 3 collaborative neural processing units. In particular, to obtain higher functions, form the motion fusion block in the middle of the function transfer block to enjoy the benefits of eliminating background noise, motion capture, and eliminating information deficit. In addition, to deal with some of the shortcomings (such as non-adaptive updating) of existing sparse coding optimizers and to take advantage of the advantages of neural network (such as parallel computation), design a unique continuous neural network, which will be told as a thin illustration of a docent dictionary by proposing a consistent iterative rule of hard threshold (adaptive ISTA) and the reformulation of adaptive ISTA as a substitute for long-term memory (LSTM). As far as we know, this may be one of the first works to link the `1-solvers and LSTM and offer new insights into LSTM and model-based refinement (or so-called differential programming), but primarily in the form of detection-based sparse secret writing anomaly. In-depth, experiments show the progressive performance of our technique in the task of detecting abnormal events.