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 4351–4375 of 4925 papers

TitleStatusHype
LSQ++: Lower running time and higher recall in multi-codebook quantizationCode0
Nearly Lossless Adaptive Bit SwitchingCode0
Scaling Image Tokenizers with Grouped Spherical QuantizationCode0
Additive Noise Annealing and Approximation Properties of Quantized Neural NetworksCode0
Discrete Cross-Modal Alignment Enables Zero-Shot Speech TranslationCode0
NestQuant: Post-Training Integer-Nesting Quantization for On-Device DNNCode0
LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling MatricesCode0
Who's a Good Boy? Reinforcing Canine Behavior in Real-Time using Machine LearningCode0
Unsupervised Speech Representation Pooling Using Vector QuantizationCode0
xCOMET-lite: Bridging the Gap Between Efficiency and Quality in Learned MT Evaluation MetricsCode0
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural NetworksCode0
Discrete, compositional, and symbolic representations through attractor dynamicsCode0
NeUQI: Near-Optimal Uniform Quantization Parameter InitializationCode0
DiscQuant: A Quantization Method for Neural Networks Inspired by Discrepancy TheoryCode0
Neural Architecture Codesign for Fast Physics ApplicationsCode0
Quantization-Based Regularization for AutoencodersCode0
Digital and Hybrid Precoding Designs in Massive MIMO with Low-Resolution ADCsCode0
Quantization Effects on Neural Networks Perception: How would quantization change the perceptual field of vision models?Code0
Diffusion Models as Stochastic Quantization in Lattice Field TheoryCode0
Comprehensive SNN Compression Using ADMM Optimization and Activity RegularizationCode0
Comprehensive Comparisons of Uniform Quantization in Deep Image CompressionCode0
Extracting Usable Predictions from Quantized Networks through Uncertainty Quantification for OOD DetectionCode0
Neural Network Activation Quantization with Bitwise Information BottlenecksCode0
Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural NetworksCode0
Neural Network Assisted Lifting Steps For Improved Fully Scalable Lossy Image Compression in JPEG 2000Code0
Show:102550
← PrevPage 175 of 197Next →
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