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Found 16 papers, 3 papers with code
ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks
Low-rank compression, a popular model compression technique that produces compact convolutional neural networks (CNNs) with low rankness, has been well-studied in the literature.
COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision Models
Attention-based vision models, such as Vision Transformer (ViT) and its variants, have shown promising performance in various computer vision tasks.
HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks
By interpreting automatic rank selection from an architecture search perspective, we develop an end-to-end solution to determine the suitable layer-wise ranks in a differentiable and hardware-aware way.
GOHSP: A Unified Framework of Graph and Optimization-based Heterogeneous Structured Pruning for Vision Transformer
We first develop a graph-based ranking for measuring the importance of attention heads, and the extracted importance information is further integrated to an optimization-based procedure to impose the heterogeneous structured sparsity patterns on the ViT models.
Algorithm and Hardware Co-Design of Energy-Efficient LSTM Networks for Video Recognition with Hierarchical Tucker Tensor Decomposition
Meanwhile, compared with the state-of-the-art tensor decomposed model-oriented hardware TIE, our proposed FDHT-LSTM architecture achieves better performance in throughput, area efficiency and energy efficiency, respectively on LSTM-Youtube workload.
CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness
Considering the co-importance of model compactness and robustness in practical applications, several prior works have explored to improve the adversarial robustness of the sparse neural networks.
Robot Motion Planning as Video Prediction: A Spatio-Temporal Neural Network-based Motion Planner
Despite the current development in this direction, the efficient capture and processing of important sequential and spatial information, in a direct and simultaneous way, is still relatively under-explored.
HODEC: Towards Efficient High-Order DEcomposed Convolutional Neural Networks
High-order decomposition is a widely used model compression approach towards compact convolutional neural networks (CNNs).
CHIP: CHannel Independence-based Pruning for Compact Neural Networks
Filter pruning has been widely used for neural network compression because of its enabled practical acceleration.
SPARK: co-exploring model SPArsity and low-RanKness for compact neural networks
Based on the observations and outcomes from our analysis, we then propose SPARK, a unified DNN compression framework that can simultaneously capture model SPArsity and low-RanKness in an efficient way.
Towards Efficient Tensor Decomposition-Based DNN Model Compression with Optimization Framework
Notably, on CIFAR-100, with 2. 3X and 2. 4X compression ratios, our models have 1. 96% and 2. 21% higher top-1 accuracy than the original ResNet-20 and ResNet-32, respectively.
Towards Extremely Compact RNNs for Video Recognition with Fully Decomposed Hierarchical Tucker Structure
Although various prior works have been proposed to reduce the RNN model sizes, executing RNN models in resource-restricted environments is still a very challenging problem.
Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding
Recently deep neural networks have been successfully applied in channel coding to improve the decoding performance.
Compressing Recurrent Neural Networks Using Hierarchical Tucker Tensor Decomposition
Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling.
TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation
Secondly, we propose TGAN that integrates deep convolutional generative adversarial networks and tensor super-resolution in a cascading manner, to generate high-quality images from random distributions.
Seismic facies recognition based on prestack data using deep convolutional autoencoder
Prestack seismic data carries much useful information that can help us find more complex atypical reservoirs.
