Implementation of Lightweight Convolutional Neural Networks via Layer-Wise Differentiable Compression
نویسندگان
چکیده
منابع مشابه
Towards Convolutional Neural Networks Compression via Global Error Reconstruction
In recent years, convolutional neural networks (CNNs) have achieved remarkable success in various applications such as image classification, object detection, object parsing and face alignment. Such CNN models are extremely powerful to deal with massive amounts of training data by using millions and billions of parameters. However, these models are typically deficient due to the heavy cost in m...
متن کاملCompression Artifacts Removal Using Convolutional Neural Networks
This paper shows that it is possible to train large and deep convolutional neural networks (CNN) for JPEG compression artifacts reduction, and that such networks can provide significantly better reconstruction quality compared to previously used smaller networks as well as to any other state-of-the-art methods. We were able to train networks with 8 layers in a single step and in relatively shor...
متن کاملBeyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation
Despite the tremendous achievements of deep convolutional neural networks (CNNs) in most of computer vision tasks, understanding how they actually work remains a significant challenge. In this paper, we propose a novel two-step visualization method that aims to shed light on how deep CNNs recognize images and the objects therein. We start out with a layer-wise relevance propagation (LRP) step w...
متن کاملDeep Convolutional Neural Networks with Layer-Wise Context Expansion and Attention
In this paper, we propose a deep convolutional neural network (CNN) with layer-wise context expansion and location-based attention, for large vocabulary speech recognition. In our model each higher layer uses information from broader contexts, along both the time and frequency dimensions, than its immediate lower layer. We show that both the layer-wise context expansion and the location-based a...
متن کاملInterpreting Convolutional Neural Networks Through Compression
Convolutional neural networks (CNNs) achieve state-of-the-art performance in a wide variety of tasks in computer vision. However, interpreting CNNs still remains a challenge. This is mainly due to the large number of parameters in these networks. Here, we investigate the role of compression and particularly pruning filters in the interpretation of CNNs. We exploit our recently-proposed greedy s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Sensors
سال: 2021
ISSN: 1424-8220
DOI: 10.3390/s21103464