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 1726–1750 of 4925 papers

TitleStatusHype
From Audio to Photoreal Embodiment: Synthesizing Humans in ConversationsCode7
Retraining-free Model Quantization via One-Shot Weight-Coupling LearningCode1
Model-Free Learning for the Linear Quadratic Regulator over Rate-Limited Channels—0
MOC-RVQ: Multilevel Codebook-Assisted Digital Generative Semantic CommunicationCode1
PredToken: Predicting Unknown Tokens and Beyond with Coarse-to-Fine Iterative Decoding—0
Are Conventional SNNs Really Efficient? A Perspective from Network Quantization—0
Transferable Structural Sparse Adversarial Attack Via Exact Group Sparsity TrainingCode1
Boosting Spike Camera Image Reconstruction from a Perspective of Dealing with Spike FluctuationsCode1
JointSQ: Joint Sparsification-Quantization for Distributed LearningCode1
PikeLPN: Mitigating Overlooked Inefficiencies of Low-Precision Neural Networks—0
General Point Model Pretraining with Autoencoding and AutoregressiveCode0
Data-Free Quantization via Pseudo-label Filtering—0
Spatial-Aware Regression for Keypoint LocalizationCode1
Enhancing Post-training Quantization Calibration through Contrastive Learning—0
Reg-PTQ: Regression-specialized Post-training Quantization for Fully Quantized Object Detector—0
HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes—0
Compact Neural Graphics Primitives with Learned Hash Probing—0
Fast Inference of Mixture-of-Experts Language Models with OffloadingCode4
TinyGPT-V: Efficient Multimodal Large Language Model via Small BackbonesCode3
FALCON: Feature-Label Constrained Graph Net Collapse for Memory Efficient GNNsCode0
LeanVec: Searching vectors faster by making them fitCode2
Context-aware Communication for Multi-agent Reinforcement LearningCode1
A-SDM: Accelerating Stable Diffusion through Redundancy Removal and Performance Optimization—0
Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization—0
Efficient Asynchronous Federated Learning with Sparsification and Quantization—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