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 4601–4625 of 4925 papers

TitleStatusHype
Recent Advances in Efficient Computation of Deep Convolutional Neural Networks—0
Mixed-Resolution Image Representation and Compression with Convolutional Neural Networks—0
Alternating Multi-bit Quantization for Recurrent Neural Networks—0
A notion of stability for k-means clustering—0
Fast binary embeddings, and quantized compressed sensing with structured matrices—0
Quantization Error as a Metric for Dynamic Precision Scaling in Neural Net Training—0
Zero-Delay Gaussian Joint Source-Channel Coding for the Interference Channel—0
Quantization Under the Real-world Measure: Fast and Accurate Valuation of Long-dated Contracts—0
BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized WeightsCode0
Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask WeightsCode0
Hyperspectral recovery from RGB images using Gaussian Processes—0
Conditional Probability Models for Deep Image CompressionCode0
Automatic Parameter Tying in Neural Networks—0
Convergence rate of sign stochastic gradient descent for non-convex functions—0
WSNet: Learning Compact and Efficient Networks with Weight Sampling—0
Discrete-Valued Neural Networks Using Variational Inference—0
Variational Network Quantization—0
Iterative Deep Compression : Compressing Deep Networks for Classification and Semantic Segmentation—0
Adaptive Quantization of Neural Networks—0
Automated flow for compressing convolution neural networks for efficient edge-computation with FPGA—0
Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image—0
Data Clustering using a Hybrid of Fuzzy C-Means and Quantum-behaved Particle Swarm Optimization—0
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only InferenceCode1
Exploiting Modern Hardware for High-Dimensional Nearest Neighbor Search—0
AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training—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