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 3301–3325 of 4925 papers

TitleStatusHype
Discrete-Valued Neural Communication—0
Multi-modality Deep Restoration of Extremely Compressed Face Videos—0
Q-SpiNN: A Framework for Quantizing Spiking Neural Networks—0
Exact Backpropagation in Binary Weighted Networks with Group Weight TransformationsCode0
BAGUA: Scaling up Distributed Learning with System RelaxationsCode1
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
Secure Quantized Training for Deep LearningCode1
Orthonormal Product Quantization Network for Scalable Face Image RetrievalCode0
Power Law Graph Transformer for Machine Translation and Representation LearningCode0
Post-Training Quantization for Vision Transformer—0
LNS-Madam: Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update—0
PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation—0
Preliminary study on using vector quantization latent spaces for TTS/VC systems with consistent performance—0
Countering Adversarial Examples: Combining Input Transformation and Noisy Training—0
Transform-Based Feature Map Compression for CNN Inference—0
Quantization Aware Training, ERNIE and Kurtosis Regularizer: a short empirical study—0
APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor CoresCode1
On Minimizing Symbol Error Rate Over Fading Channels with Low-Resolution Quantization—0
Over-the-Air Computation via Cloud Radio Access Networks—0
Witten-type topological field theory of self-organized criticality for stochastic neural networks—0
Efficient Inference via Universal LSH Kernel—0
Tensor Learning-based Precoder Codebooks for FD-MIMO Systems—0
Communication Efficient SGD via Gradient Sampling With Bayes Prior—0
QPP: Real-Time Quantization Parameter Prediction for Deep Neural Networks—0
Event-Based Bispectral Photometry Using Temporally Modulated Illumination—0
Show:102550
← PrevPage 133 of 197Next →
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