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 601–625 of 4925 papers

TitleStatusHype
MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector QuantizationCode3
Exploiting Latent Properties to Optimize Neural Codecs—0
BlockDialect: Block-wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM InferenceCode0
TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer—0
Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning—0
Self-Supervised Learning for Color Spike Camera ReconstructionCode0
Frequency-Biased Synergistic Design for Image Compression and Compensation—0
PillarHist: A Quantization-aware Pillar Feature Encoder based on Height-aware Histogram—0
Secret Lies in Color: Enhancing AI-Generated Images Detection with Color Distribution Analysis—0
Enhancing Diversity for Data-free Quantization—0
Multirate Neural Image Compression with Adaptive Lattice Vector Quantization—0
Efficient Decoupled Feature 3D Gaussian Splatting via Hierarchical Compression—0
Rethinking Diffusion for Text-Driven Human Motion Generation: Redundant Representations, Evaluation, and Masked Autoregression—0
STEPS: Sequential Probability Tensor Estimation for Text-to-Image Hard Prompt Search—0
DynScene: Scalable Generation of Dynamic Robotic Manipulation Scenes for Embodied AI—0
CacheQuant: Comprehensively Accelerated Diffusion Models—0
Intuitive Analysis of the Quantization-based Optimization: From Stochastic and Quantum Mechanical Perspective—0
PQD: Post-training Quantization for Efficient Diffusion Models—0
Improving Acoustic Scene Classification in Low-Resource Conditions—0
Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization—0
DoTA: Weight-Decomposed Tensor Adaptation for Large Language Models—0
PTQ4VM: Post-Training Quantization for Visual MambaCode1
Pushing the Envelope of Low-Bit LLM via Dynamic Error Compensation—0
IMSSA: Deploying modern state-space models on memristive in-memory compute hardware—0
Data-Free Group-Wise Fully Quantized Winograd Convolution via Learnable Scales—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