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

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Showing 576–600 of 4925 papers

TitleStatusHype
Designing Large Foundation Models for Efficient Training and Inference: A SurveyCode1
It's All In the Teacher: Zero-Shot Quantization Brought Closer to the TeacherCode1
Conditional Coding and Variable Bitrate for Practical Learned Video CodingCode1
IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion ModelsCode1
IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network QuantizationCode1
Bayesian Bits: Unifying Quantization and PruningCode1
Compression with Bayesian Implicit Neural RepresentationsCode1
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation ModelsCode1
SimCC: a Simple Coordinate Classification Perspective for Human Pose EstimationCode1
Join the High Accuracy Club on ImageNet with A Binary Neural Network TicketCode1
Improving Neural Network Efficiency via Post-Training Quantization With Adaptive Floating-PointCode1
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer ProgrammingCode1
Compress Any Segment Anything Model (SAM)Code1
Compressing LLMs: The Truth is Rarely Pure and Never SimpleCode1
Improving Detail in Pluralistic Image Inpainting with Feature DequantizationCode1
A Memory Efficient Baseline for Open Domain Question AnsweringCode1
BAND-2k: Banding Artifact Noticeable Database for Banding Detection and Quality AssessmentCode1
Image Compression with Recurrent Neural Network and Generalized Divisive NormalizationCode1
Comprehensive Graph-conditional Similarity Preserving Network for Unsupervised Cross-modal HashingCode1
COMQ: A Backpropagation-Free Algorithm for Post-Training QuantizationCode1
INT-FP-QSim: Mixed Precision and Formats For Large Language Models and Vision TransformersCode1
CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-ResolutionCode1
BBS: Bi-directional Bit-level Sparsity for Deep Learning AccelerationCode1
Improvements to Target-Based 3D LiDAR to Camera CalibrationCode1
Inducing Systematicity in Transformers by Attending to Structurally Quantized EmbeddingsCode1
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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