Neural Network Compression
74 papers with code • 1 benchmarks • 1 datasets
Benchmarks
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Libraries
Use these libraries to find Neural Network Compression models and implementations
CIFAR-10
Latest papers with no code
Towards 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.
SPC-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.
EPSD: Early Pruning with Self-Distillation for Efficient Model Compression
Instead of a simple combination of pruning and SD, EPSD enables the pruned network to favor SD by keeping more distillable weights before training to ensure better distillation of the pruned network.
Convolutional Neural Network Compression via Dynamic Parameter Rank Pruning
To address these issues, we propose an efficient training method for CNN compression via dynamic parameter rank pruning.
Balanced and Deterministic Weight-sharing Helps Network Performance
We also present two novel hash functions, the Dirichlet hash and the Neighborhood hash, and use them to demonstrate experimentally that balanced and deterministic weight-sharing helps with the performance of a neural network.
ABKD: Graph Neural Network Compression with Attention-Based Knowledge Distillation
To address this shortcoming, we propose a novel KD approach to GNN compression that we call Attention-Based Knowledge Distillation (ABKD).
Grokking as Compression: A Nonlinear Complexity Perspective
To do so, we define linear mapping number (LMN) to measure network complexity, which is a generalized version of linear region number for ReLU networks.
Quantization 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.
Survey on Computer Vision Techniques for Internet-of-Things Devices
In this paper, we survey both low-power techniques for both convolutional and transformer DNNs, and summarize the advantages, disadvantages, and open research problems.
Model Compression Methods for YOLOv5: A Review
This paper targets those interested in the practical deployment of model compression methods on YOLOv5, and in exploring different compression techniques that can be used for subsequent versions of YOLO.
