# A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification Source: [A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:17:53.140Z Authors: Yanan Sun, Bing Xue, Mengjie Zhang, Gary G. Yen Published: 2017-12-13T23:20:54.000Z ## Abstract Convolutional auto-encoders have shown their remarkable performance in stacking to deep convolutional neural networks for classifying image data during past several years. However, they are unable to construct the state-of-the-art convolutional neural networks due to their intrinsic architectures. In this regard, we propose a flexible convolutional auto-encoder by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional convolutional auto-encoder. We also design an architecture discovery method by using particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed flexible convolutional auto-encoder with much less computational resource and without any manual intervention. We use the designed architecture optimization algorithm to test the proposed flexible convolutional auto-encoder through utilizing one graphic processing unit card on four extensively used image classification datasets. Experimental results show that our work in this paper significantly outperform the peer competitors including the state-of-the-art algorithm. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()