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 1126–1150 of 4925 papers

TitleStatusHype
BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation—0
Achieving binary weight and activation for LLMs using Post-Training Quantization—0
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models—0
AccLLM: Accelerating Long-Context LLM Inference Via Algorithm-Hardware Co-Design—0
Are You Getting What You Pay For? Auditing Model Substitution in LLM APIsCode0
Bridging the Gap between Continuous and Informative Discrete Representations by Random Product Quantization—0
Balancing Robustness and Efficiency in Embedded DNNs Through Activation Function Selection—0
PRIMA.CPP: Speeding Up 70B-Scale LLM Inference on Low-Resource Everyday Home ClustersCode0
Skin Color Measurement from Dermatoscopic Images: An Evaluation on a Synthetic Dataset—0
Autoregressive High-Order Finite Difference Modulo Imaging: High-Dynamic Range for Computer Vision Applications—0
Shape My Moves: Text-Driven Shape-Aware Synthesis of Human Motions—0
Efficient FPGA-accelerated Convolutional Neural Networks for Cloud Detection on CubeSats—0
Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency—0
Compressing 3D Gaussian Splatting by Noise-Substituted Vector QuantizationCode0
HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement—0
Bridging the Gap between Gaussian Diffusion Models and Universal Quantization for Image Compression—0
Moment Quantization for Video Temporal Grounding—0
When Reasoning Meets Compression: Benchmarking Compressed Large Reasoning Models on Complex Reasoning Tasks—0
LLMPi: Optimizing LLMs for High-Throughput on Raspberry Pi—0
QSViT: A Methodology for Quantizing Spiking Vision Transformers—0
Model Hemorrhage and the Robustness Limits of Large Language Models—0
Style Quantization for Data-Efficient GAN Training—0
SQuat: Subspace-orthogonal KV Cache Quantization—0
Cocktail: Chunk-Adaptive Mixed-Precision Quantization for Long-Context LLM Inference—0
NeuralGS: Bridging Neural Fields and 3D Gaussian Splatting for Compact 3D Representations—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