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

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Showing 3751–3775 of 4925 papers

TitleStatusHype
Optimization of embeddings storage for RAG systems using quantization and dimensionality reduction techniques—0
Optimization of Quantized Phase Shifts for Reconfigurable Smart Surfaces Assisted Communications—0
Optimized Cartesian K-Means—0
Optimized Feature Space Learning for Generating Efficient Binary Codes for Image Retrieval—0
Optimized learned entropy coding parameters for practical neural-based image and video compression—0
Optimized Precoding for MU-MIMO With Fronthaul Quantization—0
Optimized Product Quantization for Approximate Nearest Neighbor Search—0
Optimized Quantization in Distributed Graph Signal Filtering—0
Optimizing Byte-level Representation for End-to-end ASR—0
Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrieval—0
Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features—0
Optimizing JPEG Quantization for Classification Networks—0
Optimizing Large Language Models through Quantization: A Comparative Analysis of PTQ and QAT Techniques—0
Optimizing Large Language Model Training Using FP4 Quantization—0
Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques—0
Optimizing MRF-ASL Scan Design for Precise Quantification of Brain Hemodynamics using Neural Network Regression—0
Optimizing Small Language Models for In-Vehicle Function-Calling—0
Optimizing Speech Recognition For The Edge—0
Orthus: Autoregressive Interleaved Image-Text Generation with Modality-Specific Heads—0
OSPC: Artificial VLM Features for Hateful Meme Detection—0
OuroMamba: A Data-Free Quantization Framework for Vision Mamba Models—0
Outlier-Aware Training for Low-Bit Quantization of Structural Re-Parameterized Networks—0
Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis—0
Outliers and Calibration Sets have Diminishing Effect on Quantization of Modern LLMs—0
OutlierTune: Efficient Channel-Wise Quantization for Large Language Models—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