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 1751–1775 of 4925 papers

TitleStatusHype
Cross-Layer Optimization for Fault-Tolerant Deep Learning—0
TinySAM: Pushing the Envelope for Efficient Segment Anything ModelCode2
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data HeterogeneityCode0
Towards Efficient Verification of Quantized Neural NetworksCode0
Mini-GPTs: Efficient Large Language Models through Contextual PruningCode1
Compact 3D Scene Representation via Self-Organizing Gaussian GridsCode3
SimQ-NAS: Simultaneous Quantization Policy and Neural Architecture Search—0
Find the Lady: Permutation and Re-Synchronization of Deep Neural NetworksCode0
Power-Efficient Sampling—0
Quantized Decoder in Learned Image Compression for Deterministic Reconstruction—0
StyleSinger: Style Transfer for Out-of-Domain Singing Voice SynthesisCode2
Post-Training Quantization for Re-parameterization via Coarse & Fine Weight SplittingCode0
SPT: Fine-Tuning Transformer-based Language Models Efficiently with SparsificationCode0
Adaptive Computation Modules: Granular Conditional Computation For Efficient InferenceCode0
IQNet: Image Quality Assessment Guided Just Noticeable Difference Prefiltering For Versatile Video Coding—0
ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative TasksCode2
Design Space Exploration of Low-Bit Quantized Neural Networks for Visual Place Recognition—0
USM-Lite: Quantization and Sparsity Aware Fine-tuning for Speech Recognition with Universal Speech Models—0
CBQ: Cross-Block Quantization for Large Language Models—0
IDKM: Memory Efficient Neural Network Quantization via Implicit, Differentiable k-Means—0
When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks—0
Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization—0
Decoupling Meta-Reinforcement Learning with Gaussian Task Contexts and SkillsCode0
Neural Architecture Codesign for Fast Bragg Peak Analysis—0
QMGeo: Differentially Private Federated Learning via Stochastic Quantization with Mixed Truncated Geometric Distribution—0
Show:102550
← PrevPage 71 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