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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...
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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...
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06/17/2021
Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay
This paper proposes two novel knowledge transfer techniques for class-in...
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05/08/2020
Data-Free Network Quantization With Adversarial Knowledge Distillation
Network quantization is an essential procedure in deep learning for deve...
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09/11/2019
Variable Rate Deep Image Compression With a Conditional Autoencoder
In this paper, we propose a novel variable-rate learned image compressio...
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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...
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02/21/2019
Jointly Sparse Convolutional Neural Networks in Dual Spatial-Winograd Domains
We consider the optimization of deep convolutional neural networks (CNNs...
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09/01/2018
Learning Low Precision Deep Neural Networks through Regularization
We consider the quantization of deep neural networks (DNNs) to produce l...
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05/21/2018
Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints
We consider the optimization of deep convolutional neural networks (CNNs...
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02/07/2018
Universal Deep Neural Network Compression
Compression of deep neural networks (DNNs) for memory- and computation-e...
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12/05/2016