Accuracy Evaluation of Transposed Convolution-Based Quantized Neural Networks

Abstract

Several modern applications in the field of Artificial Intelligence exploit deep learning to make accurate decisions. Recent work on compression techniques allows for deep learning applications, such as computer vision, to run on Edge Computing devices. For instance, quantizing the precision of deep learning architectures allows Edge Computing devices to achieve high throughput at low power. Quantization has been mainly focused on multilayer perceptrons and convolution-based models for classification problems. However, its impact over more complex scenarios, such as image up-sampling, is still underexplored. This paper presents a systematic evaluation of the accuracy achieved by quantized neural networks when performing image up-sampling in three different applications: image compression/decompression, synthetic image generation and semantic segmentation. Taking into account the promising attitude of learnable filters to predict pixels, transposed convolutional layers are used for up-sampling. Experimental results based on analytical metrics show that acceptable accuracies are reached with quantization spanning between 3 and 7 bits. Based on the visual inspection, the range 2–6 bits guarantees appropriate accuracy.