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 2576–2600 of 4925 papers

TitleStatusHype
Recommender systems inspired by the structure of quantum theory—0
Recommending Pre-Trained Models for IoT Devices—0
Reconfigurable co-processor architecture with limited numerical precision to accelerate deep convolutional neural networks—0
Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer—0
Reconstruction-Computation-Quantization (RCQ): A Paradigm for Low Bit Width LDPC Decoding—0
Reconstruction Condition of Quantized Signals in Unlimited Sampling Framework—0
Recovery of sparse linear classifiers from mixture of responses—0
Recurrence of Optimum for Training Weight and Activation Quantized Networks—0
Recursive Quantization for L_2 Stabilization of a Finite Capacity Stochastic Control Loop with Intermittent State Observations—0
Redistribution of Weights and Activations for AdderNet Quantization—0
Reduced bit median quantization: A middle process for Efficient Image Compression—0
Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case—0
Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression—0
Reduced Reference Quality Assessment for Point Cloud Compression—0
Reducing Communication for Split Learning by Randomized Top-k Sparsification—0
Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation—0
InfLoR-SNN: Reducing Information Loss for Spiking Neural Networks—0
Reducing the Memory Footprint of 3D Gaussian Splatting—0
Reducing the Model Order of Deep Neural Networks Using Information Theory—0
Reducing the Side-Effects of Oscillations in Training of Quantized YOLO Networks—0
Reduplication across Categories in Cantonese—0
RefQSR: Reference-based Quantization for Image Super-Resolution Networks—0
Image restoration quality assessment based on regional differential information entropy—0
Region-of-Interest Based Neural Video Compression—0
Reg-PTQ: Regression-specialized Post-training Quantization for Fully Quantized Object Detector—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