
Towards Fair Federated Learning with ZeroShot Data Augmentation
Federated learning has emerged as an important distributed learning para...
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MLPerf Mobile Inference Benchmark: Why Mobile AI Benchmarking Is Hard and What to Do About It
MLPerf Mobile is the first industrystandard opensource mobile benchmar...
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WAFFLe: Weight Anonymized Factorization for Federated Learning
In domains where data are sensitive or private, there is great value in ...
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DataFree Network Quantization With Adversarial Knowledge Distillation
Network quantization is an essential procedure in deep learning for deve...
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HyperCon: ImageToVideo Model Transfer for VideoToVideo Translation Tasks
Videotovideo translation for superresolution, inpainting, style trans...
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EndtoEnd MultiTask Denoising for the Joint Optimization of Perceptual Speech Metrics
Although supervised learning based on a deep neural network has recently...
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TGSA: Transformer with Gaussianweighted selfattention for speech enhancement
Transformer neural networks (TNN) demonstrated stateofart performance ...
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Transformer with Gaussian weighted selfattention for speech enhancement
The Transformer architecture recently replaced recurrent neural networks...
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Variable Rate Deep Image Compression With a Conditional Autoencoder
In this paper, we propose a novel variablerate learned image compressio...
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TWSMNet: Deep Multitask Learning of TeleWide Stereo Matching
In this paper, we introduce the problem of estimating the real world dep...
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Deep Robust Single Image Depth Estimation Neural Network Using Scene Understanding
Single image depth estimation (SIDE) plays a crucial role in 3D computer...
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Wyner VAE: Joint and Conditional Generation with Succinct Common Representation Learning
A new variational autoencoder (VAE) model is proposed that learns a succ...
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AMNet: Deep Atrous Multiscale Stereo Disparity Estimation Networks
In this paper, a new deep learning architecture for stereo disparity est...
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Jointly Sparse Convolutional Neural Networks in Dual SpatialWinograd Domains
We consider the optimization of deep convolutional neural networks (CNNs...
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DNResNet: Efficient Deep Residual Network for Image Denoising
A deep learning approach to blind denoising of images without complete k...
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Learning Low Precision Deep Neural Networks through Regularization
We consider the quantization of deep neural networks (DNNs) to produce l...
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Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints
We consider the optimization of deep convolutional neural networks (CNNs...
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Universal Deep Neural Network Compression
Compression of deep neural networks (DNNs) for memory and computatione...
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CTSRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution
We propose methodologies to train highly accurate and efficient deep con...
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BridgeNets: StudentTeacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition
Despite the remarkable progress achieved on automatic speech recognition...
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Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition
In this paper, a novel architecture for a deep recurrent neural network,...
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Towards the Limit of Network Quantization
Network quantization is one of network compression techniques to reduce ...
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Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection
We propose a deep neural network fusion architecture for fast and robust...
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Mostafa ElKhamy
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