RandomForestMLP: An Ensemble-Based Multi-Layer Perceptron Against Curse of Dimensionality
Georgia Institute of Technology（佐治亚理工学院）；Télécom Paris（巴黎高等电信学院）
英文摘要：
We present a novel and practical deep learning pipeline termed RandomForestMLP. This core trainable classification engine consists of a convolutional neural network backbone followed by an ensemble-based multi-layer perceptrons core for the classification task. It is designed in the context of self and semi-supervised learning tasks to avoid overfitting while training on very small datasets. The paper details the architecture of the RandomForestMLP and present different strategies for neural network decision aggregation. Then, it assesses its robustness to overfitting when trained on realistic image datasets and compares its classification performance with existing regular classifiers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
