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 1851–1875 of 4925 papers

TitleStatusHype
Feature Quantization for Defending Against Distortion of Images—0
Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own—0
Comparing Iterative and Least-Squares Based Phase Noise Tracking in Receivers with 1-bit Quantization and Oversampling—0
FoVolNet: Fast Volume Rendering using Foveated Deep Neural Networks—0
High-performance deep spiking neural networks with 0.3 spikes per neuron—0
Comparing Fisher Information Regularization with Distillation for DNN Quantization—0
Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data—0
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking—0
FP8-BERT: Post-Training Quantization for Transformer—0
ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers—0
FD-LSCIC: Frequency Decomposition-based Learned Screen Content Image Compression—0
FP8 versus INT8 for efficient deep learning inference—0
FDD Massive MIMO: How to Optimally Combine UL Pilot and Limited DL CSI Feedback?—0
FPGA Resource-aware Structured Pruning for Real-Time Neural Networks—0
FD Cell-Free mMIMO: Analysis and Optimization—0
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking—0
FPSAttention: Training-Aware FP8 and Sparsity Co-Design for Fast Video Diffusion—0
FPTQ: Fine-grained Post-Training Quantization for Large Language Models—0
FPTQuant: Function-Preserving Transforms for LLM Quantization—0
FP=xINT:A Low-Bit Series Expansion Algorithm for Post-Training Quantization—0
FCN-Pose: A Pruned and Quantized CNN for Robot Pose Estimation for Constrained Devices—0
FBQuant: FeedBack Quantization for Large Language Models—0
Compact Representation for Image Classification: To Choose or to Compress?—0
FBI: Fingerprinting models with Benign Inputs—0
Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms—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