论文标题
RandomForestMLP：一种基于合奏的多层感知器针对维度的诅咒
RandomForestMLP: An Ensemble-Based Multi-Layer Perceptron Against Curse of Dimensionality
论文作者
论文摘要
我们提出了一种新颖而实用的深度学习管道，称为Randomforestmlp。该核心可训练的分类引擎由卷积神经网络骨架组成，然后是基于合奏的多层感知器核心，用于分类任务。它是在自我和半监督的学习任务的背景下设计的，以避免在很小的数据集上培训时过度拟合。本文详细介绍了RandomForestMLP的体系结构，并为神经网络决策汇总提供了不同的策略。然后，它评估其在逼真的图像数据集接受培训时过度适应的稳健性，并将其分类性能与现有常规分类器进行比较。
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.
