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 4901–4925 of 4925 papers

TitleStatusHype
GenQ: Quantization in Low Data Regimes with Generative Synthetic DataCode0
QEBVerif: Quantization Error Bound Verification of Neural NetworksCode0
QEFT: Quantization for Efficient Fine-Tuning of LLMsCode0
Robust open-set classification for encrypted traffic fingerprintingCode0
Boosting CNN-based primary quantization matrix estimation of double JPEG images via a classification-like architectureCode0
A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent FlowCode0
DNN Feature Map Compression using Learned Representation over GF(2)Code0
Stacked Quantizers for Compositional Vector CompressionCode0
Data Efficient Stagewise Knowledge DistillationCode0
Flexible framework for audio reconstructionCode0
BlockDialect: Block-wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM InferenceCode0
Exploration into Translation-Equivariant Image QuantizationCode0
Word2Bits - Quantized Word VectorsCode0
Q-HyViT: Post-Training Quantization of Hybrid Vision Transformers with Bridge Block Reconstruction for IoT SystemsCode0
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
Find the Lady: Permutation and Re-Synchronization of Deep Neural NetworksCode0
STAR: Learning Diverse Robot Skill Abstractions through Rotation-Augmented Vector QuantizationCode0
Diversity in deep generative models and generative AICode0
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier IntegralsCode0
QLESS: A Quantized Approach for Data Valuation and Selection in Large Language Model Fine-TuningCode0
Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly DetectionCode0
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication BudgetCode0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Filtering Empty Camera Trap Images in Embedded SystemsCode0
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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