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 2326–2350 of 4925 papers

TitleStatusHype
Causal Speech Enhancement with Predicting Semantics based on Quantized Self-supervised Learning Features—0
An Overview of Neural Network Compression—0
Efficient Systolic Array Based on Decomposable MAC for Quantized Deep Neural Networks—0
Efficient Super Resolution Using Binarized Neural Network—0
An Overview of Datatype Quantization Techniques for Convolutional Neural Networks—0
Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction—0
Starting Positions Matter: A Study on Better Weight Initialization for Neural Network Quantization—0
Efficient Storage of Fine-Tuned Models via Low-Rank Approximation of Weight Residuals—0
Efficient Speech Representation Learning with Low-Bit Quantization—0
Efficient Approximate Search for Sets of Vectors—0
Efficient Quantum Approximate kNN Algorithm via Granular-Ball Computing—0
A Novel Unified Model for Multi-exposure Stereo Coding Based on Low Rank Tucker-ALS and 3D-HEVC—0
Efficient Quantization Strategies for Latent Diffusion Models—0
Can Large Language Models Understand Context?—0
A Novel Structure-Agnostic Multi-Objective Approach for Weight-Sharing Compression in Deep Neural Networks—0
Efficient Point Transformer for Large-scale 3D Scene Understanding—0
Efficient On-the-fly Category Retrieval using ConvNets and GPUs—0
Can General-Purpose Large Language Models Generalize to English-Thai Machine Translation ?—0
A Novel Physics-based Channel Model for Reconfigurable Intelligent Surface-assisted Multi-user Communication Systems—0
Adaptive Joint Optimization for 3D Reconstruction with Differentiable Rendering—0
Efficient Neural PDE-Solvers using Quantization Aware Training—0
Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review—0
Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data—0
Efficient Neural Compression with Inference-time Decoding—0
CAMBI: Contrast-aware Multiscale Banding Index—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