Papers Neural Network Compression
“Neural Network Compression” 태그가 달린 논문 216편 · 필터 해제
An Efficient Real-Time Object Detection Framework on Resource-Constricted Hardware Devices via Software and Hardware Co-design
The fast development of object detection techniques has attracted attention to developing efficient Deep Neural Networks (DNNs). However, the current state-of-the-art DNN models can not provide a balanced solution among …
Model CompressionNeural Network CompressionObjectobject-detection+3Tiled Bit Networks: Sub-Bit Neural Network Compression Through Reuse of Learnable Binary Vectors
Binary Neural Networks (BNNs) enable efficient deep learning by saving on storage and computational costs. However, as the size of neural networks continues to grow, meeting computational requirements remains a challenge…
GPUNeural Network CompressionQuantizationTime Series ForecastingThe Impact of Quantization and Pruning on Deep Reinforcement Learning Models
Deep reinforcement learning (DRL) has achieved remarkable success across various domains, such as video games, robotics, and, recently, large language models. However, the computational costs and memory requirements of D…
Deep Reinforcement LearningModel CompressionNeural Network CompressionQuantizationNeural Network Compression for Reinforcement Learning Tasks
In real applications of Reinforcement Learning (RL), such as robotics, low latency and energy efficient inference is very desired. The use of sparsity and pruning for optimizing Neural Network inference, and particularly…
Neural Network Compressionreinforcement-learningReinforcement LearningReinforcement Learning (RL)Torch2Chip: An End-to-end Customizable Deep Neural Network Compression and Deployment Toolkit for Prototype Hardware Accelerator Design
The development of model compression is continuously motivated by the evolution of various neural network accelerators with ASIC or FPGA. On the algorithm side, the ultimate goal of quantization or pruning is acceleratin…
Model CompressionNeural Network CompressionQuantizationTowards Explaining Deep Neural Network Compression Through a Probabilistic Latent Space
Despite the impressive performance of deep neural networks (DNNs), their computational complexity and storage space consumption have led to the concept of network compression. While DNN compression techniques such as pru…
Neural Network CompressionSPC-NeRF: Spatial Predictive Compression for Voxel Based Radiance Field
Representing the Neural Radiance Field (NeRF) with the explicit voxel grid (EVG) is a promising direction for improving NeRFs. However, the EVG representation is not efficient for storage and transmission because of the …
Image CompressionNeRFNeural Network CompressionQuantizationTowards Meta-Pruning via Optimal Transport
Structural pruning of neural networks conventionally relies on identifying and discarding less important neurons, a practice often resulting in significant accuracy loss that necessitates subsequent fine-tuning efforts. …
Neural Network CompressionEPSD: Early Pruning with Self-Distillation for Efficient Model Compression
Neural network compression techniques, such as knowledge distillation (KD) and network pruning, have received increasing attention. Recent work `Prune, then Distill' reveals that a pruned student-friendly teacher network…
Knowledge DistillationModel CompressionNetwork PruningNeural Network CompressionConvolutional Neural Network Compression via Dynamic Parameter Rank Pruning
While Convolutional Neural Networks (CNNs) excel at learning complex latent-space representations, their over-parameterization can lead to overfitting and reduced performance, particularly with limited data. This, alongs…
Model CompressionNeural Network CompressionBalanced and Deterministic Weight-sharing Helps Network Performance
Weight-sharing plays a significant role in the success of many deep neural networks, by increasing memory efficiency and incorporating useful inductive priors about the problem into the network. But understanding how wei…
Neural Network CompressionABKD: Graph Neural Network Compression with Attention-Based Knowledge Distillation
Graph Neural Networks (GNNs) have proven to be quite versatile for a variety of applications, including recommendation systems, fake news detection, drug discovery, and even computer vision. Due to the expanding size of …
Drug DiscoveryFake News DetectionGraph Neural NetworkKnowledge Distillation+2Grokking as Compression: A Nonlinear Complexity Perspective
We attribute grokking, the phenomenon where generalization is much delayed after memorization, to compression. To do so, we define linear mapping number (LMN) to measure network complexity, which is a generalized version…
AttributeMemorizationNeural Network CompressionCausal-DFQ: Causality Guided Data-free Network Quantization
Model quantization, which aims to compress deep neural networks and accelerate inference speed, has greatly facilitated the development of cumbersome models on mobile and edge devices. There is a common assumption in qua…
Data Free QuantizationNeural Network CompressionQuantizationA Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations
Modern deep neural networks, particularly recent large language models, come with massive model sizes that require significant computational and storage resources. To enable the deployment of modern models on resource-co…
Adversarial RobustnessNetwork PruningNeural Network CompressionQuantization Aware Factorization for Deep Neural Network Compression
Tensor decomposition of convolutional and fully-connected layers is an effective way to reduce parameters and FLOP in neural networks. Due to memory and power consumption limitations of mobile or embedded devices, the qu…
Neural Network CompressionQuantizationTensor DecompositionSurvey on Computer Vision Techniques for Internet-of-Things Devices
Deep neural networks (DNNs) are state-of-the-art techniques for solving most computer vision problems. DNNs require billions of parameters and operations to achieve state-of-the-art results. This requirement makes DNNs e…
Neural Network CompressionSurveyModel Compression Methods for YOLOv5: A Review
Over the past few years, extensive research has been devoted to enhancing YOLO object detectors. Since its introduction, eight major versions of YOLO have been introduced with the purpose of improving its accuracy and ef…
Knowledge DistillationmodelModel CompressionNetwork Pruning+2Lightweight Attribute Localizing Models for Pedestrian Attribute Recognition
Pedestrian Attribute Recognition (PAR) deals with the problem of identifying features in a pedestrian image. It has found interesting applications in person retrieval, suspect re-identification and soft biometrics. In th…
AttributeNeural Network CompressionPedestrian Attribute RecognitionPerson Retrieval+2Neural Network Compression using Binarization and Few Full-Precision Weights
Quantization and pruning are two effective Deep Neural Networks model compression methods. In this paper, we propose Automatic Prune Binarization (APB), a novel compression technique combining quantization with pruning. …
BinarizationCPUModel CompressionNeural Network Compression+1