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 3401–3450 of 4925 papers

TitleStatusHype
Vector Quantization Methods for Access Point Placement in Cell-Free Massive MIMO Systems—0
Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression—0
Vector Quantization With Self-Attention for Quality-Independent Representation Learning—0
G2P-DDM: Generating Sign Pose Sequence from Gloss Sequence with Discrete Diffusion Model—0
Discrete Graph Auto-Encoder—0
Vector Quantized Image-to-Image Translation—0
Vector Quantized Semantic Communication System—0
Vector Quantized Spectral Clustering applied to Soybean Whole Genome Sequences—0
Verification of Bit-Flip Attacks against Quantized Neural Networks—0
Verifying Low-dimensional Input Neural Networks via Input Quantization—0
Verifying Quantized Neural Networks using SMT-Based Model Checking—0
Versatile Physics-based Character Control with Hybrid Latent Representation—0
Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization—0
Vertical Layering of Quantized Neural Networks for Heterogeneous Inference—0
Video Coding for Machines: Partial transmission of SIFT features—0
Video Coding for Machines with Feature-Based Rate-Distortion Optimization—0
Video Compression With Entropy-Constrained Neural Representations—0
ViM-VQ: Efficient Post-Training Vector Quantization for Visual Mamba—0
Virtual Codec Supervised Re-Sampling Network for Image Compression—0
Vision Foundation Models as Effective Visual Tokenizers for Autoregressive Image Generation—0
Vision-Language Models for Edge Networks: A Comprehensive Survey—0
Vision Transformer-based Semantic Communications With Importance-Aware Quantization—0
Vis-TOP: Visual Transformer Overlay Processor—0
Visual Time Series Forecasting: An Image-driven Approach—0
Visualisation and knowledge discovery from interpretable models—0
ViT2Hash: Unsupervised Information-Preserving Hashing—0
VMAF-based Bitrate Ladder Estimation for Adaptive Streaming—0
VocalEyes: Enhancing Environmental Perception for the Visually Impaired through Vision-Language Models and Distance-Aware Object Detection—0
Volumetric Calculation of Quantization Error in 3-D Vision Systems—0
VQ4DiT: Efficient Post-Training Vector Quantization for Diffusion Transformers—0
VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling—0
VQEL: Enabling Self-Developed Symbolic Language in Agents through Vector Quantization in Emergent Language Games—0
VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits—0
VQ-NeRF: Neural Reflectance Decomposition and Editing with Vector Quantization—0
VQ-NeRF: Vector Quantization Enhances Implicit Neural Representations—0
VQN: Variable Quantization Noise for Neural Network Compression—0
vqSGD: Vector Quantized Stochastic Gradient Descent—0
VQSynery: Robust Drug Synergy Prediction With Vector Quantization Mechanism—0
VQTalker: Towards Multilingual Talking Avatars through Facial Motion Tokenization—0
VQUNet: Vector Quantization U-Net for Defending Adversarial Atacks by Regularizing Unwanted Noise—0
VRVVC: Variable-Rate NeRF-Based Volumetric Video Compression—0
VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference—0
Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications—0
Warped-Linear Models for Time Series Classification—0
WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking—0
Wav2vec-C: A Self-supervised Model for Speech Representation Learning—0
Wavelet-denoising on hardware devices with Perfect Reconstruction, low latency and adaptive thresholding—0
Wavelet Feature Maps Compression for Low Bandwidth Convolutional Neural Networks—0
Weak target detection with multi-bit quantization in colocated MIMO radar—0
Weighted-Entropy-Based Quantization for Deep Neural Networks—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