Search Results for author:
Found 12 papers, 6 papers with code
Rethinking RGB Color Representation for Image Restoration Models
Image restoration models are typically trained with a pixel-wise distance loss defined over the RGB color representation space, which is well known to be a source of blurry and unrealistic textures in the restored images.
Semantic-Aware Dynamic Parameter for Video Inpainting Transformer
Recent learning-based video inpainting approaches have achieved considerable progress.
MEIL-NeRF: Memory-Efficient Incremental Learning of Neural Radiance Fields
Under such incremental learning scenarios, neural networks are known to suffer catastrophic forgetting: easily forgetting previously seen data after training with new data.
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.
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.
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.
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.
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.
Domain Adaptation of Learned Features for Visual Localization
We tackle the problem of visual localization under changing conditions, such as time of day, weather, and seasons.
Scene-Adaptive Video Frame Interpolation via Meta-Learning
Finally, we show that our meta-learning framework can be easily employed to any video frame interpolation network and can consistently improve its performance on multiple benchmark datasets.
Learning to Forget for Meta-Learning
Model-agnostic meta-learning (MAML) tackles the problem by formulating prior knowledge as a common initialization across tasks, which is then used to quickly adapt to unseen tasks.
