research
∙
08/12/2023
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
Transfer learning via fine-tuning pre-trained transformer models has gai...
share
research
∙
10/26/2022
Zero-Shot Learning of a Conditional Generative Adversarial Network for Data-Free Network Quantization
We propose a novel method for training a conditional generative adversar...
research
∙
10/11/2022
Toward Sustainable Continual Learning: Detection and Knowledge Repurposing of Similar Tasks
Most existing works on continual learning (CL) focus on overcoming the c...
research
∙
06/17/2021
Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay
This paper proposes two novel knowledge transfer techniques for class-in...
research
∙
04/27/2021
Towards Fair Federated Learning with Zero-Shot Data Augmentation
Federated learning has emerged as an important distributed learning para...
research
∙
12/03/2020
MLPerf Mobile Inference Benchmark: Why Mobile AI Benchmarking Is Hard and What to Do About It
MLPerf Mobile is the first industry-standard open-source mobile benchmar...
research
∙
08/13/2020
WAFFLe: Weight Anonymized Factorization for Federated Learning
In domains where data are sensitive or private, there is great value in ...
research
∙
05/08/2020
Data-Free Network Quantization With Adversarial Knowledge Distillation
Network quantization is an essential procedure in deep learning for deve...
research
∙
12/10/2019
HyperCon: Image-To-Video Model Transfer for Video-To-Video Translation Tasks
Video-to-video translation for super-resolution, inpainting, style trans...
research
∙
10/23/2019
End-to-End Multi-Task Denoising for the Joint Optimization of Perceptual Speech Metrics
Although supervised learning based on a deep neural network has recently...
research
∙
10/13/2019
T-GSA: Transformer with Gaussian-weighted self-attention for speech enhancement
Transformer neural networks (TNN) demonstrated state-of-art performance ...
research
∙
10/13/2019
Transformer with Gaussian weighted self-attention for speech enhancement
The Transformer architecture recently replaced recurrent neural networks...
research
∙
09/11/2019
Variable Rate Deep Image Compression With a Conditional Autoencoder
In this paper, we propose a novel variable-rate learned image compressio...
research
∙
06/11/2019
TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching
In this paper, we introduce the problem of estimating the real world dep...
research
∙
06/07/2019
Deep Robust Single Image Depth Estimation Neural Network Using Scene Understanding
Single image depth estimation (SIDE) plays a crucial role in 3D computer...
research
∙
05/27/2019
Wyner VAE: Joint and Conditional Generation with Succinct Common Representation Learning
A new variational autoencoder (VAE) model is proposed that learns a succ...
research
∙
04/19/2019
AMNet: Deep Atrous Multiscale Stereo Disparity Estimation Networks
In this paper, a new deep learning architecture for stereo disparity est...
research
∙
02/21/2019
Jointly Sparse Convolutional Neural Networks in Dual Spatial-Winograd Domains
We consider the optimization of deep convolutional neural networks (CNNs...
research
∙
10/16/2018
DN-ResNet: Efficient Deep Residual Network for Image Denoising
A deep learning approach to blind denoising of images without complete k...
research
∙
09/01/2018
Learning Low Precision Deep Neural Networks through Regularization
We consider the quantization of deep neural networks (DNNs) to produce l...
research
∙
05/21/2018
Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints
We consider the optimization of deep convolutional neural networks (CNNs...
research
∙
02/07/2018
Universal Deep Neural Network Compression
Compression of deep neural networks (DNNs) for memory- and computation-e...
research
∙
11/11/2017
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution
We propose methodologies to train highly accurate and efficient deep con...
research
∙
10/27/2017
BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition
Despite the remarkable progress achieved on automatic speech recognition...
research
∙
01/10/2017
Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition
In this paper, a novel architecture for a deep recurrent neural network,...
research
∙
12/05/2016
Towards the Limit of Network Quantization
Network quantization is one of network compression techniques to reduce ...
research
∙
10/11/2016
