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 1351–1375 of 4925 papers

TitleStatusHype
Athena: Efficient Block-Wise Post-Training Quantization for Large Language Models Using Second-Order Matrix Derivative Information—0
BiSup: Bidirectional Quantization Error Suppression for Large Language Models—0
OAC: Output-adaptive Calibration for Accurate Post-training Quantization—0
A rescaling-invariant Lipschitz bound based on path-metrics for modern ReLU network parameterizations—0
SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language ModelsCode2
ASI++: Towards Distributionally Balanced End-to-End Generative Retrieval—0
ZipCache: Accurate and Efficient KV Cache Quantization with Salient Token IdentificationCode1
Rate-Adaptive Quantization: A Multi-Rate Codebook Adaptation for Vector Quantization-based Generative ModelsCode1
PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM CompressionCode5
TerDiT: Ternary Diffusion Models with TransformersCode2
MultiCast: Zero-Shot Multivariate Time Series Forecasting Using LLMs—0
LG-VQ: Language-Guided Codebook Learning—0
Embedding Compression for Efficient Re-Identification—0
Bracket Diffusion: HDR Image Generation by Consistent LDR Denoising—0
Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval—0
Integer Scale: A Free Lunch for Faster Fine-grained Quantization of LLMs—0
MiniCache: KV Cache Compression in Depth Dimension for Large Language Models—0
Mitigating Quantization Errors Due to Activation Spikes in GLU-Based LLMsCode0
AdpQ: A Zero-shot Calibration Free Adaptive Post Training Quantization Method for LLMs—0
QGait: Toward Accurate Quantization for Gait Recognition with Binarized Input—0
Adaptive Wireless Image Semantic Transmission and Over-The-Air Testing—0
Communication-Efficient Federated Learning via Clipped Uniform QuantizationCode0
Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers—0
eXmY: A Data Type and Technique for Arbitrary Bit Precision Quantization—0
Two Heads are Better Than One: Neural Networks Quantization with 2D Hilbert Curve-based Output Representation—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