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Enhanced Deep Residual Networks for Single Image Super-Resolution
This paper develops an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods, and proposes a new multi-scale deepsuper-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model. Expand
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results
This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution. Expand
Channel Attention Is All You Need for Video Frame Interpolation
A simple but effective deep neural network for video frame interpolation, which is end-to-end trainable and is free from a motion estimation network component, and achieves outstanding performance compared to the existing models with a component for optical flow computation. Expand
Task-Aware Image Downscaling
This paper proposes an auto-encoder-based framework that enables joint learning of the downscaling network and the upsc scaling network to maximize the restoration performance and validates the model’s generalization capability by applying it to the task of image colorization. Expand
Meta-Learning with Adaptive Hyperparameters
A new weight update rule is proposed that greatly enhances the fast adaptation process in MAML framework, inner-loop optimization (or fast adaptation), and introduces a small meta-network that can adaptively generate per-step hyperparameters: learning rate and weight decay coefficients. Expand
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 compositionalExpand
AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results
This paper reviews the first AIM challenge on video temporal super-resolution (frame interpolation) with a focus on the proposed solutions and results, and employs the REDS_VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes. Expand
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report
The target is to develop an end-to-end deep learning-based video super-resolution solutions that can achieve a realtime performance on mobile GPUs and can upscale videos to HD resolution at up to 80 FPS while demonstrating high fidelity results. Expand
DAQ: Distribution-Aware Quantization for Deep Image Super-Resolution Networks
This work proposes a novel distribution-aware quantization scheme (DAQ) which facilitates accurate training-free quantization in ultra-low precision and outperforms recent training- free and even trainingbased quantization methods to the state-of-the-art image super-resolution networks in ultra -low precision. Expand
Searching for Controllable Image Restoration Networks
Using the proposed task-agnostic and taskspecific pruning schemes together significantly reduces the FLOPs and the actual latency of inference compared to the baseline, and makes the GPU latency faster on 4K-resolution images. Expand