Gaussian RAM: Lightweight Image Classification via Stochastic Retina-Inspired Glimpse and Reinforcement Learning

• URL: http://arxiv.org/abs/2011.06190v1
• Date: Thu, 12 Nov 2020 04:27:06 GMT
• Title: Gaussian RAM: Lightweight Image Classification via Stochastic Retina-Inspired Glimpse and Reinforcement Learning
• Authors: Dongseok Shim and H. Jin Kim
• Abstract summary: We propose a reinforcement learning based lightweight deep neural network for large scale image classification. We evaluate the model on cluttered MNIST, Large CIFAR-10 and Large CIFAR-100 datasets.
• Score: 29.798579906253696
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Previous studies on image classification have mainly focused on the performance of the networks, not on real-time operation or model compression. We propose a Gaussian Deep Recurrent visual Attention Model (GDRAM)- a reinforcement learning based lightweight deep neural network for large scale image classification that outperforms the conventional CNN (Convolutional Neural Network) which uses the entire image as input. Highly inspired by the biological visual recognition process, our model mimics the stochastic location of the retina with Gaussian distribution. We evaluate the model on Large cluttered MNIST, Large CIFAR-10 and Large CIFAR-100 datasets which are resized to 128 in both width and height.

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