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Found 8 papers, 2 papers with code
CorrTalk: Correlation Between Hierarchical Speech and Facial Activity Variances for 3D Animation
In this study, we propose a novel framework, CorrTalk, which effectively establishes the temporal correlation between hierarchical speech features and facial activities of different intensities across distinct regions.
Dynamic Shuffle: An Efficient Channel Mixture Method
To reduce the data-dependent redundancy, we devise a dynamic shuffle module to generate data-dependent permutation matrices for shuffling.
LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch
Therefore the pruning strategy can gradually prune the network and automatically determine the appropriate pruning rates for each layer.
Compact Model Training by Low-Rank Projection with Energy Transfer
In this paper, we devise a new training method, low-rank projection with energy transfer (LRPET), that trains low-rank compressed networks from scratch and achieves competitive performance.
Weight Evolution: Improving Deep Neural Networks Training through Evolving Inferior Weight Values
Comprehensive experiments show that WE outperforms the other reactivation methods and plug-in training methods with typical convolutional neural networks, especially lightweight networks.
Deep Sampling Networks
With the powerful down-sampling process, the co-training DSN set a new state-of-the-art performance for image super-resolution.
A Joint Intrinsic-Extrinsic Prior Model for Retinex
We propose a joint intrinsic-extrinsic prior model to estimate both illumination and reflectance from an observed image.
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network
However, the previous methods mainly restore images from one single area in the low resolution (LR) input, which limits the flexibility of models to infer various scales of details for high resolution (HR) output.
