Papers Neural Network Compression
“Neural Network Compression” 태그가 달린 논문 216편 · 필터 해제
Quantum Neural Network Compression
Model compression, such as pruning and quantization, has been widely applied to optimize neural networks on resource-limited classical devices. Recently, there are growing interest in variational quantum circuits (VQC), …
Model CompressionNeural Network CompressionQuantizationA Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation
The goal of model compression is to reduce the size of a large neural network while retaining a comparable performance. As a result, computation and memory costs in resource-limited applications may be significantly redu…
Model CompressionNeural Network CompressionNeural Network Compression via Effective Filter Analysis and Hierarchical Pruning
Network compression is crucial to making the deep networks to be more efficient, faster, and generalizable to low-end hardware. Current network compression methods have two open problems: first, there lacks a theoretical…
Network PruningNeural Network CompressionWavelet Feature Maps Compression for Image-to-Image CNNs
Convolutional Neural Networks (CNNs) are known for requiring extensive computational resources, and quantization is among the best and most common methods for compressing them. While aggressive quantization (i.e., less t…
Depth EstimationNeural Network CompressionQuantizationSemantic SegmentationA Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification
Recent advancements in machine learning achieved by Deep Neural Networks (DNNs) have been significant. While demonstrating high accuracy, DNNs are associated with a huge number of parameters and computations, which leads…
image-classificationImage ClassificationNeural Network CompressionQuantizationRevisiting Random Channel Pruning for Neural Network Compression
Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of neural networks. There has been a flurry of algorithms that try to solve this practical problem, each being claimed effective in so…
Neural Network CompressionPPCD-GAN: Progressive Pruning and Class-Aware Distillation for Large-Scale Conditional GANs Compression
We push forward neural network compression research by exploiting a novel challenging task of large-scale conditional generative adversarial networks (GANs) compression. To this end, we propose a gradually shrinking GAN …
Neural Network CompressionApproximate Bisimulation Relations for Neural Networks and Application to Assured Neural Network Compression
In this paper, we propose a concept of approximate bisimulation relation for feedforward neural networks. In the framework of approximate bisimulation relation, a novel neural network merging method is developed to compu…
Neural Network CompressionRelationFew-Bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction
Memory footprint is one of the main limiting factors for large neural network training. In backpropagation, one needs to store the input to each operation in the computational graph. Every modern neural network model has…
Neural Network CompressionQuantizationNeural Network Compression of ACAS Xu Early Prototype is Unsafe: Closed-Loop Verification through Quantized State Backreachability
ACAS Xu is an air-to-air collision avoidance system designed for unmanned aircraft that issues horizontal turn advisories to avoid an intruder aircraft. Due the use of a large lookup table in the design, a neural network…
Collision AvoidanceNeural Network CompressionQuantizationToward Compact Parameter Representations for Architecture-Agnostic Neural Network Compression
This paper investigates deep neural network (DNN) compression from the perspective of compactly representing and storing trained parameters. We explore the previously overlooked opportunity of cross-layer architecture-ag…
Neural Network CompressionQuantizationVQN: Variable Quantization Noise for Neural Network Compression
Quantization refers to a set of methods that compress a neural network by representing its parameters with fewer bits. However, applying quantization to a neural network after training often leads to severe performance r…
Neural Network CompressionQuantizationNeRV: Neural Representations for Videos
We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking …
DenoisingNeural Network CompressionVideo CompressionVideo Denoising+1CHIP: CHannel Independence-based Pruning for Compact Neural Networks
Filter pruning has been widely used for neural network compression because of its enabled practical acceleration. To date, most of the existing filter pruning works explore the importance of filters via using intra-chann…
Neural Network CompressionAdaptive Distillation: Aggregating Knowledge from Multiple Paths for Efficient Distillation
Knowledge Distillation is becoming one of the primary trends among neural network compression algorithms to improve the generalization performance of a smaller student model with guidance from a larger teacher model. Thi…
Knowledge DistillationNeural Network Compressionobject-detectionObject Detection+1Semi-tensor Product-based TensorDecomposition for Neural Network Compression
The existing tensor networks adopt conventional matrix product for connection. The classical matrix product requires strict dimensionality consistency between factors, which can result in redundancy in data representatio…
Low-rank compressionNeural Network CompressionTensor NetworksPrune Your Model Before Distill It
Knowledge distillation transfers the knowledge from a cumbersome teacher to a small student. Recent results suggest that the student-friendly teacher is more appropriate to distill since it provides more transferable kno…
Knowledge DistillationmodelNeural Network CompressionConvolutional Neural Network Compression through Generalized Kronecker Product Decomposition
Modern Convolutional Neural Network (CNN) architectures, despite their superiority in solving various problems, are generally too large to be deployed on resource constrained edge devices. In this paper, we reduce memory…
image-classificationImage ClassificationKnowledge DistillationNeural Network CompressionTropical Geometrical Zonotope Reduction as Applied to Neural Network Compression.
Recently, tropical geometry had a significant impact on the study of deep neural networks. Motivated by this progress, we provide a novel framework for tropical polynomial approximation based on geometrical zonotope redu…
Neural Network CompressionQuantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study
Generative adversarial networks (GANs) have an enormous potential impact on digital content creation, e.g., photo-realistic digital avatars, semantic content editing, and quality enhancement of speech and images. However…
Neural Network CompressionQuantization