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 676–700 of 4925 papers

TitleStatusHype
AdANNS: A Framework for Adaptive Semantic SearchCode1
Learning to Improve Image Compression without Changing the Standard DecoderCode1
Conditional Coding and Variable Bitrate for Practical Learned Video CodingCode1
EA-3DGS: Efficient and Adaptive 3D Gaussians with Highly Enhanced Quality for outdoor scenesCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
Confounding Tradeoffs for Neural Network QuantizationCode1
BitStack: Any-Size Compression of Large Language Models in Variable Memory EnvironmentsCode1
Learning to Structure an Image with Few ColorsCode1
COMQ: A Backpropagation-Free Algorithm for Post-Training QuantizationCode1
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning ApproachCode1
Online Learned Continual Compression with Adaptive Quantization ModulesCode1
Designing Large Foundation Models for Efficient Training and Inference: A SurveyCode1
Benchmarking of DL Libraries and Models on Mobile DevicesCode1
Optimal Discrete Beamforming of RIS-Aided Wireless Communications: An Inner Product Maximization ApproachCode1
BL-JUNIPER: A CNN-Assisted Framework for Perceptual Video Coding Leveraging Block-Level JNDCode1
CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-ResolutionCode1
Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-ExplorerCode1
Learning Statistical Texture for Semantic SegmentationCode1
Least squares binary quantization of neural networksCode1
Lite Transformer with Long-Short Range AttentionCode1
Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts RemovalCode1
A Little Bit More: Bitplane-Wise Bit-Depth RecoveryCode1
Efficient and Robust Quantization-aware Training via Adaptive Coreset SelectionCode1
Learning Cross-Scale Weighted Prediction for Efficient Neural Video CompressionCode1
Compressing LLMs: The Truth is Rarely Pure and Never SimpleCode1
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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