Search Results for author:
Found 6 papers, 4 papers with code
AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-Resolution
Although image super-resolution (SR) problem has experienced unprecedented restoration accuracy with deep neural networks, it has yet limited versatile applications due to the substantial computational costs.
Overcoming Distribution Mismatch in Quantizing Image Super-Resolution Networks
Quantization is a promising approach to reduce the high computational complexity of image super-resolution (SR) networks.
CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution
In this work, to achieve high average bit-reduction with less accuracy loss, we propose a novel Content-Aware Dynamic Quantization (CADyQ) method for SR networks that allocates optimal bits to local regions and layers adaptively based on the local contents of an input image.
Attentive Fine-Grained Structured Sparsity for Image Restoration
Image restoration tasks have witnessed great performance improvement in recent years by developing large deep models.
Batch Normalization Tells You Which Filter is Important
The goal of filter pruning is to search for unimportant filters to remove in order to make convolutional neural networks (CNNs) efficient without sacrificing the performance in the process.
DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks
Quantizing deep convolutional neural networks for image super-resolution substantially reduces their computational costs.
