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Found 4 papers, 3 papers with code
Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss
Through extensive experiments, we demonstrate that our SR4IR achieves outstanding task performance by generating SR images useful for a specific image recognition task, including semantic segmentation, object detection, and image classification.
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.
