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 3901–3925 of 4925 papers

TitleStatusHype
Progressive Fine-to-Coarse Reconstruction for Accurate Low-Bit Post-Training Quantization in Vision Transformers—0
Progressive Mixed-Precision Decoding for Efficient LLM Inference—0
Progressive Neural Image Compression with Nested Quantization and Latent Ordering—0
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA—0
PROM: Prioritize Reduction of Multiplications Over Lower Bit-Widths for Efficient CNNs—0
Prompting Large Language Models for Clinical Temporal Relation Extraction—0
Prompt Tuning as User Inherent Profile Inference Machine—0
Proofread: Fixes All Errors with One Tap—0
Protecting Data from all Parties: Combining FHE and DP in Federated Learning—0
Prototype-based classifiers in the presence of concept drift: A modelling framework—0
Prototype-based Neural Network Layers: Incorporating Vector Quantization—0
Provable Privacy with Non-Private Pre-Processing—0
Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization—0
Proximity Graph Maintenance for Fast Online Nearest Neighbor Search—0
ProxyBNN: Learning Binarized Neural Networks via Proxy Matrices—0
Prune Once for All: Sparse Pre-Trained Language Models—0
Prune or quantize? Strategy for Pareto-optimally low-cost and accurate CNN—0
Pruning and Quantization for Deep Neural Network Acceleration: A Survey—0
Pruning Ternary Quantization—0
PRUNIX: Non-Ideality Aware Convolutional Neural Network Pruning for Memristive Accelerators—0
PTQ4ADM: Post-Training Quantization for Efficient Text Conditional Audio Diffusion Models—0
PTQ-SL: Exploring the Sub-layerwise Post-training Quantization—0
Publishing Efficient On-device Models Increases Adversarial Vulnerability—0
PulseDL-II: A System-on-Chip Neural Network Accelerator for Timing and Energy Extraction of Nuclear Detector Signals—0
Push for Quantization: Deep Fisher Hashing—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