SOTAVerified

Quantization

Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).

Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers

Papers

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Showing 3951–4000 of 4925 papers

TitleStatusHype
Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks—0
Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks—0
Communication-efficient k-Means for Edge-based Machine Learning—0
Communication Efficient SGD via Gradient Sampling With Bayes Prior—0
Communication-Efficient Split Learning via Adaptive Feature-Wise Compression—0
Communication-efficient Variance-reduced Stochastic Gradient Descent—0
Compact and Robust Deep Learning Architecture for Fluorescence Lifetime Imaging and FPGA Implementation—0
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks—0
Compact Neural Graphics Primitives with Learned Hash Probing—0
Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms—0
Compact Representation for Image Classification: To Choose or to Compress?—0
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking—0
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking—0
Comparing Fisher Information Regularization with Distillation for DNN Quantization—0
Comparing Iterative and Least-Squares Based Phase Noise Tracking in Receivers with 1-bit Quantization and Oversampling—0
Comparison of 14 different families of classification algorithms on 115 binary datasets—0
Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other—0
Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners—0
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission—0
CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting—0
Component Training of Turbo Autoencoders—0
Composite Code Sparse Autoencoders for first stage retrieval—0
Composite Correlation Quantization for Efficient Multimodal Retrieval—0
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models—0
Comprehensive Survey of Model Compression and Speed up for Vision Transformers—0
Compressed Domain Prior-Guided Video Super-Resolution for Cloud Gaming Content—0
Compressed Models Decompress Race Biases: What Quantized Models Forget for Fair Face Recognition—0
Compressed Particle-Based Federated Bayesian Learning and Unlearning—0
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data—0
Compressed Video Super-Resolution based on Hierarchical Encoding—0
Compressing Deep Convolutional Networks using Vector Quantization—0
Compressing Language Models for Specialized Domains—0
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks—0
Compressing Neural Machine Translation Models with 4-bit Precision—0
Compressing Pre-trained Transformers via Low-Bit NxM Sparsity for Natural Language Understanding—0
Compressing Recurrent Neural Networks for FPGA-accelerated Implementation in Fluorescence Lifetime Imaging—0
Compressing Unknown Images With Product Quantizer for Efficient Zero-Shot Classification—0
Compressing VAE-Based Out-of-Distribution Detectors for Embedded Deployment—0
Compressing Weight-updates for Image Artifacts Removal Neural Networks—0
Compression-based Privacy Preservation for Distributed Nash Equilibrium Seeking in Aggregative Games—0
Compression for Better: A General and Stable Lossless Compression Framework—0
Compression of Acoustic Event Detection Models with Low-rank Matrix Factorization and Quantization Training—0
Compression of Acoustic Event Detection Models With Quantized Distillation—0
Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints—0
Compression of Deep Learning Models for Text: A Survey—0
Compression of Deep Neural Networks for Image Instance Retrieval—0
Compression of Deep Neural Networks on the Fly—0
Compression of Generative Pre-trained Language Models via Quantization—0
Compression of Recurrent Neural Networks for Efficient Language Modeling—0
Compression of Site-Specific Deep Neural Networks for Massive MIMO Precoding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03—Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31—Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97—Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71—Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2—Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51—Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17—Unverified
8Xception W8A8Top-1 Accuracy (%)78.97—Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88—Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04—Unverified
2DTQMAP0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3—Unverified
2OutEffHop-Bert_basePerplexity6.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92—Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8—Unverified