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 1326–1350 of 4925 papers

TitleStatusHype
Efficient Neural PDE-Solvers using Quantization Aware Training—0
Efficient On-the-fly Category Retrieval using ConvNets and GPUs—0
CEGI: Measuring the trade-off between efficiency and carbon emissions for SLMs and VLMs—0
CEG4N: Counter-Example Guided Neural Network Quantization Refinement—0
CDQuant: Greedy Coordinate Descent for Accurate LLM Quantization—0
CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning—0
An Overview on IEEE 802.11bf: WLAN Sensing—0
CBQ: Cross-Block Quantization for Large Language Models—0
Causal Speech Enhancement with Predicting Semantics based on Quantized Self-supervised Learning Features—0
An Overview of Neural Network Compression—0
Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction—0
An Overview of Datatype Quantization Techniques for Convolutional Neural Networks—0
Starting Positions Matter: A Study on Better Weight Initialization for Neural Network Quantization—0
Efficient Neural Compression with Inference-time Decoding—0
Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review—0
Efficient Point Transformer for Large-scale 3D Scene Understanding—0
Efficiently Scaling Transformer Inference—0
Discrete Audio Tokens: More Than a Survey!—0
Efficient Machine Translation with Model Pruning and Quantization—0
A Novel Unified Model for Multi-exposure Stereo Coding Based on Low Rank Tucker-ALS and 3D-HEVC—0
STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs—0
Efficient Match Kernel between Sets of Features for Visual Recognition—0
Can Large Language Models Understand Context?—0
A Novel Structure-Agnostic Multi-Objective Approach for Weight-Sharing Compression in Deep Neural Networks—0
Can General-Purpose Large Language Models Generalize to English-Thai Machine Translation ?—0
Show:102550
← PrevPage 54 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