Deep Forest with Hashing Screening and Window Screening
Pengfei Ma, Youxi Wu, Yan Li, Lei Guo, He Jiang, Xingquan Zhu, and, Xindong Wu

TL;DR
This paper introduces HW-Forest, a deep learning model that uses hashing and window screening strategies to reduce redundancy in feature vectors, leading to faster processing and higher accuracy.
Contribution
The paper proposes HW-Forest, combining hashing and window screening to efficiently eliminate redundant features and improve deep forest performance without extensive hyperparameter tuning.
Findings
HW-Forest reduces time cost and memory usage.
It achieves higher accuracy than comparable models.
The self-adaptive window screening improves robustness across datasets.
Abstract
As a novel deep learning model, gcForest has been widely used in various applications. However, the current multi-grained scanning of gcForest produces many redundant feature vectors, and this increases the time cost of the model. To screen out redundant feature vectors, we introduce a hashing screening mechanism for multi-grained scanning and propose a model called HW-Forest which adopts two strategies, hashing screening and window screening. HW-Forest employs perceptual hashing algorithm to calculate the similarity between feature vectors in hashing screening strategy, which is used to remove the redundant feature vectors produced by multi-grained scanning and can significantly decrease the time cost and memory consumption. Furthermore, we adopt a self-adaptive instance screening strategy to improve the performance of our approach, called window screening, which can achieve higher…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Video Analysis and Summarization · Video Surveillance and Tracking Methods
