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 2426–2450 of 4925 papers

TitleStatusHype
Breaking the Limits of Quantization-Aware Defenses: QADT-R for Robustness Against Patch-Based Adversarial Attacks in QNNs—0
An Investigation on Different Underlying Quantization Schemes for Pre-trained Language Models—0
Adaptive Dataset Quantization—0
Efficient-Adam: Communication-Efficient Distributed Adam—0
Efficient Adaptive Activation Rounding for Post-Training Quantization—0
Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection—0
Efficiency Meets Fidelity: A Novel Quantization Framework for Stable Diffusion—0
Effects of VLSI Circuit Constraints on Temporal-Coding Multilayer Spiking Neural Networks—0
Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model—0
An Inquiry into Datacenter TCO for LLM Inference with FP8—0
Effect of Weight Quantization on Learning Models by Typical Case Analysis—0
Effect of Signal Quantization on Performance Measures of a 1st Order One Dimensional Differential Microphone Array—0
BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics—0
Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations—0
Brain-inspired reverse adversarial examples—0
An Intra-BRNN and GB-RVQ Based END-TO-END Neural Audio Codec—0
Abstractive summarization from Audio Transcription—0
Effective Quantization for Diffusion Models on CPUs—0
Effective Quantization Approaches for Recurrent Neural Networks—0
Effective Interplay between Sparsity and Quantization: From Theory to Practice—0
Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation—0
Effective and Fast: A Novel Sequential Single Path Search for Mixed-Precision Quantization—0
Effective and Efficient Mixed Precision Quantization of Speech Foundation Models—0
Boost Vision Transformer with GPU-Friendly Sparsity and Quantization—0
An Inter-Layer Weight Prediction and Quantization for Deep Neural Networks based on a Smoothly Varying Weight Hypothesis—0
Show:102550
← PrevPage 98 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