Search Results for author:
Found 15 papers, 7 papers with code
Learning with Noisy Labels: Interconnection of Two Expectation-Maximizations
In this framework, we provide a proper objective function and an optimization algorithm based on two expectation-maximization (EM) cycles.
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.
Controllable Image Enhancement
Specifically, an encoder-decoder framework encodes the retouching skills into latent codes and decodes them into the parameters of image signal processing (ISP) functions.
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.
Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning
The problem lies in that each application and task may require different auxiliary loss function, especially when tasks are diverse and distinct.
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report
Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services.
Motion-Aware Dynamic Architecture for Efficient Frame Interpolation
Video frame interpolation aims to synthesize accurate intermediate frames given a low-frame-rate video.
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.
Searching for Controllable Image Restoration Networks
Diverse user preferences over images have recently led to a great amount of interest in controlling the imagery effects for image restoration tasks.
Meta-Learning with Adaptive Hyperparameters
Despite its popularity, several recent works question the effectiveness of MAML when test tasks are different from training tasks, thus suggesting various task-conditioned methodology to improve the initialization.
Channel Attention Is All You Need for Video Frame Interpolation
Prevailing video frame interpolation techniques rely heavily on optical flow estimation and require additional model complexity and computational cost; it is also susceptible to error propagation in challenging scenarios with large motion and heavy occlusion.
Fine-Grained Neural Architecture Search
We present an elegant framework of fine-grained neural architecture search (FGNAS), which allows to employ multiple heterogeneous operations within a single layer and can even generate compositional feature maps using several different base operations.
Task-Aware Image Downscaling
Our framework is efficient, and it can be generalized to handle an arbitrary image resizing operation.
Enhanced Deep Residual Networks for Single Image Super-Resolution
Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN).
Ranked #1 on
Image Super-Resolution
on DIV2K val - 4x upscaling
(PSNR metric)
