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 1576–1600 of 4925 papers

TitleStatusHype
Deep-Learned Compression for Radio-Frequency Signal Classification—0
FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization—0
Towards efficient deep autoencoders for multivariate time series anomaly detection—0
Neural Network Assisted Lifting Steps For Improved Fully Scalable Lossy Image Compression in JPEG 2000Code0
Better Schedules for Low Precision Training of Deep Neural Networks—0
A Hierarchical Federated Learning Approach for the Internet of Things—0
On the Compressibility of Quantized Large Language Models—0
Extracting Usable Predictions from Quantized Networks through Uncertainty Quantification for OOD DetectionCode0
LLM-PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive QuantizationCode1
IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens IntactCode3
"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel ApproachCode1
BasedAI: A decentralized P2P network for Zero Knowledge Large Language Models (ZK-LLMs)—0
NeuraLUT: Hiding Neural Network Density in Boolean Synthesizable FunctionsCode1
Variable-Rate Learned Image Compression with Multi-Objective Optimization and Quantization-Reconstruction Offsets—0
T3DNet: Compressing Point Cloud Models for Lightweight 3D Recognition—0
Ef-QuantFace: Streamlined Face Recognition with Small Data and Low-Bit Precision—0
No Token Left Behind: Reliable KV Cache Compression via Importance-Aware Mixed Precision Quantization—0
FlattenQuant: Breaking Through the Inference Compute-bound for Large Language Models with Per-tensor Quantization—0
Evaluating Quantized Large Language ModelsCode2
Inpainting Computational Fluid Dynamics with Deep Learning—0
Neural Video Compression with Feature Modulation—0
Adaptive quantization with mixed-precision based on low-cost proxy—0
Rethinking Mutual Information for Language Conditioned Skill Discovery on Imitation Learning—0
Distortion-Controlled Dithering with Reduced Recompression Rate—0
SPC-NeRF: Spatial Predictive Compression for Voxel Based Radiance Field—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