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 2376–2400 of 4925 papers

TitleStatusHype
An Overview on IEEE 802.11bf: WLAN Sensing—0
Zero-Shot Sharpness-Aware Quantization for Pre-trained Language Models—0
VQ-NeRF: Neural Reflectance Decomposition and Editing with Vector Quantization—0
Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning—0
A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge—0
Functional Invariants to Watermark Large Transformers—0
TEQ: Trainable Equivalent Transformation for Quantization of LLMs—0
Image Compression using only Attention based Neural Networks—0
Robustness and Approximation of Discrete-time Mean-field Games under Discounted Cost Criterion—0
One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language ModelsCode0
LL-VQ-VAE: Learnable Lattice Vector-Quantization For Efficient Representations—0
A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification—0
Cost-Driven Hardware-Software Co-Optimization of Machine Learning Pipelines—0
QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources—0
Adaptive Quantization for Key Generation in Low-Power Wide-Area Networks—0
Distillation Improves Visual Place Recognition for Low Quality ImagesCode0
Efficient-VQGAN: Towards High-Resolution Image Generation with Efficient Vision Transformers—0
Vector Quantized Multi-modal Guidance for Alzheimer’s Disease Diagnosis Based on Feature ImputationCode0
Dual Grained Quantization: Efficient Fine-Grained Quantization for LLM—0
Sub-token ViT Embedding via Stochastic Resonance TransformersCode0
Hadamard Domain Training with Integers for Class Incremental Quantized Learning—0
Robustness-Guided Image Synthesis for Data-Free Quantization—0
VaSAB: The variable size adaptive information bottleneck for disentanglement on speech and singing voice—0
Learning A Disentangling Representation For PU Learning—0
Soft Convex Quantization: Revisiting Vector Quantization with Convex Optimization—0
Show:102550
← PrevPage 96 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