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 1426–1450 of 4925 papers

TitleStatusHype
FP4DiT: Towards Effective Floating Point Quantization for Diffusion TransformersCode0
Autoregressive Co-Training for Learning Discrete Speech RepresentationsCode0
Deep Learning as a Mixed Convex-Combinatorial Optimization ProblemCode0
Floating-Point Quantization Analysis of Multi-Layer Perceptron Artificial Neural NetworksCode0
FlexRound: Learnable Rounding based on Element-wise Division for Post-Training QuantizationCode0
FLoCoRA: Federated learning compression with low-rank adaptationCode0
Foundations of Large Language Model Compression -- Part 1: Weight QuantizationCode0
Deep Image Compression via End-to-End LearningCode0
Focused Quantization for Sparse CNNsCode0
FlashEval: Towards Fast and Accurate Evaluation of Text-to-image Diffusion Generative ModelsCode0
FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAsCode0
Flexible framework for audio reconstructionCode0
A2Q: Accumulator-Aware Quantization with Guaranteed Overflow AvoidanceCode0
Deep Hashing via Householder QuantizationCode0
Flexible Mixed Precision Quantization for Learned Image CompressionCode0
FPQVAR: Floating Point Quantization for Visual Autoregressive Model with FPGA Hardware Co-designCode0
Filtering Empty Camera Trap Images in Embedded SystemsCode0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data HeterogeneityCode0
FEDZIP: A Compression Framework for Communication-Efficient Federated LearningCode0
Deep Convolutional AutoEncoder-based Lossy Image CompressionCode0
Federated Learning via Plurality VoteCode0
Find the Lady: Permutation and Re-Synchronization of Deep Neural NetworksCode0
Efficient Cross-Modal Retrieval via Deep Binary Hashing and QuantizationCode0
Deep Compressive Autoencoder for Action Potential Compression in Large-Scale Neural RecordingCode0
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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