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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4151–4175 of 4925 papers

TitleStatusHype
Deep Neural Network Capacity—0
Deep Neural Network Models Compression—0
Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications—0
Deep neural networks algorithms for stochastic control problems on finite horizon: convergence analysis—0
Deep neural networks are robust to weight binarization and other non-linear distortions—0
Deep Perceptual Preprocessing for Video Coding—0
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model—0
Deep Residual Hashing—0
Deep Saliency Hashing—0
Deep Signal Recovery with One-Bit Quantization—0
Deep Spherical Quantization for Image Search—0
Deep Task-Based Quantization—0
DeepTwist: Learning Model Compression via Occasional Weight Distortion—0
Deep Unfolding with Kernel-based Quantization in MIMO Detection—0
Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming—0
Deep Visual-Semantic Quantization for Efficient Image Retrieval—0
Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions—0
Defensive Quantization: When Efficiency Meets Robustness—0
Degree-Quant: Quantization-Aware Training for Graph Neural Networks—0
DeltaDQ: Ultra-High Delta Compression for Fine-Tuned LLMs via Group-wise Dropout and Separate Quantization—0
Delving into Channels: Exploring Hyperparameter Space of Channel Bit Widths with Linear Complexity—0
Demystifying and Generalizing BinaryConnect—0
Demystifying Singular Defects in Large Language Models—0
Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications—0
Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning—0
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All datasetsImageNetCIFAR-10Wiki-40BAgeDB-30CFP-FPCOCO (Common Objects in Context)IJB-BIJB-CKnowledge-based:LFW

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