Search Results for author:
Found 13 papers, 1 papers with code
PPG-to-ECG Signal Translation for Continuous Atrial Fibrillation Detection via Attention-based Deep State-Space Modeling
Here, we propose a subject-independent attention-based deep state-space model to translate PPG signals to corresponding ECG waveforms.
Zero-Shot Learning of a Conditional Generative Adversarial Network for Data-Free Network Quantization
We propose a novel method for training a conditional generative adversarial network (CGAN) without the use of training data, called zero-shot learning of a CGAN (ZS-CGAN).
Toward Sustainable Continual Learning: Detection and Knowledge Repurposing of Similar Tasks
This results in the eventual prohibitive expansion of the knowledge repository if we consider learning from a long sequence of tasks.
Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay
In the conventional generative replay, the generative model is pre-trained for old data and shared in extra memory for later incremental learning.
Data-Free Network Quantization With Adversarial Knowledge Distillation
The synthetic data are generated from a generator, while no data are used in training the generator and in quantization.
Wyner VAE: A Variational Autoencoder with Succinct Common Representation Learning
A new variational autoencoder (VAE) model is proposed that learns a succinct common representation of two correlated data variables for conditional and joint generation tasks.
Variable Rate Deep Image Compression With a Conditional Autoencoder
Our model also shows comparable and sometimes better performance than the state-of-the-art learned image compression models that deploy multiple networks trained for varying rates.
Learning with Succinct Common Representation Based on Wyner's Common Information
A new bimodal generative model is proposed for generating conditional and joint samples, accompanied with a training method with learning a succinct bottleneck representation.
Jointly Sparse Convolutional Neural Networks in Dual Spatial-Winograd Domains
We consider the optimization of deep convolutional neural networks (CNNs) such that they provide good performance while having reduced complexity if deployed on either conventional systems with spatial-domain convolution or lower-complexity systems designed for Winograd convolution.
Learning Sparse Low-Precision Neural Networks With Learnable Regularization
In training low-precision networks, gradient descent in the backward pass is performed with high-precision weights while quantized low-precision weights and activations are used in the forward pass to calculate the loss function for training.
Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints
In particular, the proposed framework produces one compressed model whose convolutional filters can be made sparse either in the spatial domain or in the Winograd domain.
Universal Deep Neural Network Compression
In this paper, we investigate lossy compression of deep neural networks (DNNs) by weight quantization and lossless source coding for memory-efficient deployment.
Towards the Limit of Network Quantization
Network quantization is one of network compression techniques to reduce the redundancy of deep neural networks.
