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 1551–1575 of 4925 papers

TitleStatusHype
Exploring Post-Training Quantization of Protein Language ModelsCode0
DAQ: Density-Aware Post-Training Weight-Only Quantization For LLMsCode0
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range ContentCode0
Expansion Quantization Network: An Efficient Micro-emotion Annotation and Detection FrameworkCode0
Explaining Reject Options of Learning Vector Quantization ClassifiersCode0
Exploring Quantization and Mapping Synergy in Hardware-Aware Deep Neural Network AcceleratorsCode0
ACIQ: Analytical Clipping for Integer Quantization of neural networksCode0
Exact Backpropagation in Binary Weighted Networks with Group Weight TransformationsCode0
Evaluating Quantized Large Language Models for Code Generation on Low-Resource Language BenchmarksCode0
Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspectiveCode0
CUCL: Codebook for Unsupervised Continual LearningCode0
Evaluating Large Language Models on the Frame and Symbol Grounding Problems: A Zero-shot BenchmarkCode0
Error Diffusion Halftoning Against Adversarial ExamplesCode0
Rediscovering Hashed Random Projections for Efficient Quantization of Contextualized Sentence EmbeddingsCode0
Reducing Inference Energy Consumption Using Dual Complementary CNNsCode0
Reducing Storage of Pretrained Neural Networks by Rate-Constrained Quantization and Entropy CodingCode0
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural NetworksCode0
Equal Bits: Enforcing Equally Distributed Binary Network WeightsCode0
A Transfer Learning and Explainable Solution to Detect mpox from Smartphones imagesCode0
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search SpacesCode0
Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte CarloCode0
enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural NetworksCode0
Estimation and Restoration of Unknown Nonlinear Distortion using DiffusionCode0
EXAQ: Exponent Aware Quantization For LLMs AccelerationCode0
Cross-Modal Discrete Representation Learning—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