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 3151–3200 of 4925 papers

TitleStatusHype
TwinDNN: A Tale of Two Deep Neural Networks—0
Twin Network Augmentation: A Novel Training Strategy for Improved Spiking Neural Networks and Efficient Weight Quantization—0
Two-Bit RIS-Aided Communications at 3.5GHz: Some Insights from the Measurement Results Under Multiple Practical Scenes—0
Two Dimensional Array Imaging with Beam Steered Data—0
Two Heads are Better Than One: Neural Networks Quantization with 2D Hilbert Curve-based Output Representation—0
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models—0
Two-layer Near-lossless HDR Coding with Backward Compatibility to JPEG—0
Two-Stage Hashing for Fast Document Retrieval—0
Two-stage iterative Procrustes match algorithm and its application for VQ-based speaker verification—0
Two-Stage Learning for Uplink Channel Estimation in One-Bit Massive MIMO—0
UAV-Assisted Real-Time Disaster Detection Using Optimized Transformer Model—0
UDC: Unified DNAS for Compressible TinyML Models—0
ULMRec: User-centric Large Language Model for Sequential Recommendation—0
Ultra-Lightweight Speech Separation via Group Communication—0
Ultra-low Latency Adaptive Local Binary Spiking Neural Network with Accuracy Loss Estimator—0
Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA—0
Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors—0
Ultra-Low Precision 4-bit Training of Deep Neural Networks—0
Ultra-low Precision Multiplication-free Training for Deep Neural Networks—0
Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs—0
Uncertainty-Aware Deep Video Compression with Ensembles—0
Uncertainty Estimation in Multi-Agent Distributed Learning—0
Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor—0
Unconstrained Face Recognition using ASURF and Cloud-Forest Classifier optimized with VLAD—0
Understanding Flatness in Generative Models: Its Role and Benefits—0
Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases—0
Understanding the Difficulty of Low-Precision Post-Training Quantization for LLMs—0
Understanding the Impact of Post-Training Quantization on Large Language Models—0
Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks—0
Understanding Unconventional Preprocessors in Deep Convolutional Neural Networks for Face Identification—0
UniCode: Learning a Unified Codebook for Multimodal Large Language Models—0
UniCompress: Enhancing Multi-Data Medical Image Compression with Knowledge Distillation—0
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization—0
Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization—0
Unified Data-Free Compression: Pruning and Quantization without Fine-Tuning—0
Unified learning-based lossy and lossless JPEG recompression—0
Unified Stochastic Framework for Neural Network Quantization and Pruning—0
Uniform-Precision Neural Network Quantization via Neural Channel Expansion—0
Unifying KV Cache Compression for Large Language Models with LeanKV—0
UnifySpeech: A Unified Framework for Zero-shot Text-to-Speech and Voice Conversion—0
UniHM: Universal Human Motion Generation with Object Interactions in Indoor Scenes—0
UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks—0
Universal Deep Neural Network Compression—0
Universality of Layer-Level Entropy-Weighted Quantization Beyond Model Architecture and Size—0
Universal Joint Source-Channel Coding for Modulation-Agnostic Semantic Communication—0
Universally Quantized Neural Compression—0
Unleashing Dynamic Range and Resolution in Unlimited Sensing Framework via Novel Hardware—0
Unlimited Sampling Radar: a Real-Time End-to-End Demonstrator—0
Unlocking Efficient Large Inference Models: One-Bit Unrolling Tips the Scales—0
Enhancing Multimodal Unified Representations for Cross Modal Generalization—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