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 2651–2675 of 4925 papers

TitleStatusHype
CSQ: Centered Symmetric Quantization for Extremely Low Bit Neural Networks—0
CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification—0
CSR:Achieving 1 Bit Key-Value Cache via Sparse Representation—0
CTMQ: Cyclic Training of Convolutional Neural Networks with Multiple Quantization Steps—0
CURSOR-BASED ADAPTIVE QUANTIZATION FOR DEEP NEURAL NETWORK—0
Curvature in the Looking-Glass: Optimal Methods to Exploit Curvature of Expectation in the Loss Landscape—0
Custom Gradient Estimators are Straight-Through Estimators in Disguise—0
D^2MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving—0
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning—0
DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation—0
DART: Disentanglement of Accent and Speaker Representation in Multispeaker Text-to-Speech—0
DASNet: Dynamic Activation Sparsity for Neural Network Efficiency Improvement—0
Data Augmentations in Deep Weight Spaces—0
Data Clustering using a Hybrid of Fuzzy C-Means and Quantum-behaved Particle Swarm Optimization—0
Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI—0
Data-Driven Depth Map Refinement via Multi-Scale Sparse Representation—0
Data-driven Dynamic Event-triggered Control—0
Data-Driven Sparsity-Based Restoration of JPEG-Compressed Images in Dual Transform-Pixel Domain—0
Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks—0
Data-Free Group-Wise Fully Quantized Winograd Convolution via Learnable Scales—0
Data-free mixed-precision quantization using novel sensitivity metric—0
Data-Free Network Compression via Parametric Non-Uniform Mixed Precision Quantization—0
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning—0
Data-Free Quantization via Pseudo-label Filtering—0
Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization—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