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 351–375 of 4925 papers

TitleStatusHype
Robust Machine Unlearning for Quantized Neural Networks via Adaptive Gradient Reweighting with Similar Labels—0
Quantization-Free Autoregressive Action TransformerCode0
MAG: Multi-Modal Aligned Autoregressive Co-Speech Gesture Generation without Vector Quantization—0
CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting—0
ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning—0
ML-SpecQD: Multi-Level Speculative Decoding with Quantized Drafts—0
Versatile Physics-based Character Control with Hybrid Latent Representation—0
ACT360: An Efficient 360-Degree Action Detection and Summarization Framework for Mission-Critical Training and Debriefing—0
Stabilizing Quantization-Aware Training by Implicit-Regularization on Hessian Matrix—0
Understanding Flatness in Generative Models: Its Role and Benefits—0
Pathology Image Compression with Pre-trained Autoencoders—0
Automated Tomato Maturity Estimation Using an Optimized Residual Model with Pruning and Quantization Techniques—0
Dual Codebook VQ: Enhanced Image Reconstruction with Reduced Codebook Size—0
OuroMamba: A Data-Free Quantization Framework for Vision Mamba Models—0
Global synchronization of multi-agent systems with nonlinear interactions—0
ViM-VQ: Efficient Post-Training Vector Quantization for Visual Mamba—0
Quantization for OpenAI's Whisper Models: A Comparative AnalysisCode0
Sometimes Painful but Certainly Promising: Feasibility and Trade-offs of Language Model Inference at the Edge—0
Quantitative Analysis of Deeply Quantized Tiny Neural Networks Robust to Adversarial Attacks—0
Accurate INT8 Training Through Dynamic Block-Level Fallback—0
PCGS: Progressive Compression of 3D Gaussian SplattingCode1
Quantization Design for Deep Learning-Based CSI Feedback—0
PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative ModelsCode0
VocalEyes: Enhancing Environmental Perception for the Visually Impaired through Vision-Language Models and Distance-Aware Object Detection—0
Breaking the Limits of Quantization-Aware Defenses: QADT-R for Robustness Against Patch-Based Adversarial Attacks in QNNs—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