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 976–1000 of 4925 papers

TitleStatusHype
Contrastive Quant: Quantization Makes Stronger Contrastive Learning—0
Convergence of Federated Learning over a Noisy Downlink—0
Convergence rate of sign stochastic gradient descent for non-convex functions—0
Computing with Hypervectors for Efficient Speaker Identification—0
Convergence Rates for Regularized Optimal Transport via Quantization—0
Convergence Theory of Generalized Distributed Subgradient Method with Random Quantization—0
AsymKV: Enabling 1-Bit Quantization of KV Cache with Layer-Wise Asymmetric Quantization Configurations—0
Compute-Optimal LLMs Provably Generalize Better With Scale—0
How Does Batch Normalization Help Binary Training?—0
Computation-Efficient Quantization Method for Deep Neural Networks—0
Convolutional neural network compression for natural language processing—0
Convolutional Neural Network Quantization using Generalized Gamma Distribution—0
Convolutional Neural Networks Quantization with Attention—0
Asymmetric Learning Vector Quantization for Efficient Nearest Neighbor Classification in Dynamic Time Warping Spaces—0
Aggregated Learning: A Deep Learning Framework Based on Information-Bottleneck Vector Quantization—0
Coordinated Per-Antenna Power Minimization for Multicell Massive MIMO Systems with Low-Resolution Data Converters—0
CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness—0
Coreset-Based Neural Network Compression—0
Asymptotic Analysis of One-bit Quantized Box-Constrained Precoding in Large-Scale Multi-User Systems—0
Correlated quantization for distributed mean estimation and optimization—0
Correlated Quantization for Faster Nonconvex Distributed Optimization—0
Correlation Hashing Network for Efficient Cross-Modal Retrieval—0
CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization—0
Cost-Aware Routing for Efficient Text-To-Image Generation—0
A Structurally Regularized Convolutional Neural Network for Image Classification using Wavelet-based SubBand Decomposition—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