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 1051–1075 of 4925 papers

TitleStatusHype
Loss-aware Weight Quantization of Deep NetworksCode0
Multimodal Unsupervised Domain Generalization by Retrieving Across the Modality GapCode0
Log-Time K-Means Clustering for 1D Data: Novel Approaches with Proof and ImplementationCode0
Loss Aware Post-training QuantizationCode0
Column-wise Quantization of Weights and Partial Sums for Accurate and Efficient Compute-In-Memory AcceleratorsCode0
APSQ: Additive Partial Sum Quantization with Algorithm-Hardware Co-DesignCode0
A Programmable Approach to Neural Network CompressionCode0
Mixed-Precision Quantization for Deep Vision Models with Integer Quadratic ProgrammingCode0
Linearly Converging Error Compensated SGDCode0
Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector QuantizationCode0
Lipschitz Continuity Retained Binary Neural NetworkCode0
Lightweight Client-Side Chinese/Japanese Morphological Analyzer Based on Online LearningCode0
Light Multi-segment Activation for Model CompressionCode0
Lightweight Deep Learning Based Channel Estimation for Extremely Large-Scale Massive MIMO SystemsCode0
HQOD: Harmonious Quantization for Object DetectionCode0
LISA: Learning Interpretable Skill Abstractions from LanguageCode0
LFZip: Lossy compression of multivariate floating-point time series data via improved predictionCode0
LegalEval-Q: A New Benchmark for The Quality Evaluation of LLM-Generated Legal TextCode0
Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning (Technical Report)Code0
Leveraging Continuously Differentiable Activation Functions for Learning in Quantized Noisy EnvironmentsCode0
LiFT: Lightweight, FPGA-tailored 3D object detection based on LiDAR dataCode0
LiteLMGuard: Seamless and Lightweight On-Device Prompt Filtering for Safeguarding Small Language Models against Quantization-induced Risks and VulnerabilitiesCode0
Approximate spectral clustering using both reference vectors and topology of the network generated by growing neural gasCode0
Learning Space Partitions for Nearest Neighbor SearchCode0
Approximate spectral clustering density-based similarity for noisy datasetsCode0
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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