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 2301–2325 of 4925 papers

TitleStatusHype
Alternate Learning based Sparse Semantic Communications for Visual Transmission—0
Large Language Models For Text Classification: Case Study And Comprehensive Review—0
Latency-Distortion Tradeoffs in Communicating Classification Results over Noisy Channels—0
Investigating Automatic Scoring and Feedback using Large Language Models—0
Inverted Semantic-Index for Image Retrieval—0
Deep Residual Hashing—0
Intuitive Analysis of the Quantization-based Optimization: From Stochastic and Quantum Mechanical Perspective—0
Intriguing Properties of Quantization at Scale—0
A Comprehensive Study on Quantization Techniques for Large Language Models—0
InTreeger: An End-to-End Framework for Integer-Only Decision Tree Inference—0
Investigating Disentanglement in a Phoneme-level Speech Codec for Prosody Modeling—0
Back to Simplicity: How to Train Accurate BNNs from Scratch?—0
Investigating the Impact of Quantization on Adversarial Robustness—0
IQDUBBING: Prosody modeling based on discrete self-supervised speech representation for expressive voice conversion—0
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model—0
Alternating Co-Quantization for Cross-Modal Hashing—0
Deep Signal Recovery with One-Bit Quantization—0
iRNN: Integer-only Recurrent Neural Network—0
Intra-layer Nonuniform Quantization for Deep Convolutional Neural Network—0
Background Modelling using Octree Color Quantization—0
Is Conventional SNN Really Efficient? A Perspective from Network Quantization—0
Is In-Domain Data Really Needed? A Pilot Study on Cross-Domain Calibration for Network Quantization—0
A 58.6mW Real-Time Programmable Object Detector with Multi-Scale Multi-Object Support Using Deformable Parts Model on 1920x1080 Video at 30fps—0
Is It a Free Lunch for Removing Outliers during Pretraining?—0
Lattice Quantization—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