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 3376–3400 of 4925 papers

TitleStatusHype
DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers—0
MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations—0
A probabilistic framework for dynamic quantization—0
Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training—0
MARRS: Masked Autoregressive Unit-based Reaction Synthesis—0
QVGen: Pushing the Limit of Quantized Video Generative Models—0
Auditing Black-Box LLM APIs with a Rank-Based Uniformity Test—0
Optimizing Learned Image Compression on Scalar and Entropy-Constraint Quantization—0
POLARON: Precision-aware On-device Learning and Adaptive Runtime-cONfigurable AI acceleration—0
Implementing Keyword Spotting on the MCUX947 Microcontroller with Integrated NPU—0
Discrete Audio Tokens: More Than a Survey!—0
Starting Positions Matter: A Study on Better Weight Initialization for Neural Network Quantization—0
2-bit Conformer quantization for automatic speech recognition—0
2-Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search—0
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency—0
3D Gaussian Splatting Data Compression with Mixture of Priors—0
3DMolNet: A Generative Network for Molecular Structures—0
3D Pathfinding and Collision Avoidance Using Uneven Search-space Quantization and Visual Cone Search—0
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation—0
3D representation in 512-Byte:Variational tokenizer is the key for autoregressive 3D generation—0
3D Surface Detail Enhancement From a Single Normal Map—0
3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning—0
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration—0
4-bit Quantization of LSTM-based Speech Recognition Models—0
4DGC: Rate-Aware 4D Gaussian Compression for Efficient Streamable Free-Viewpoint Video—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