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

Showing 23512375 of 4925 papers

TitleStatusHype
AWEQ: Post-Training Quantization with Activation-Weight Equalization for Large Language Models0
Crop Disease Classification using Support Vector Machines with Green Chromatic Coordinate (GCC) and Attention based feature extraction for IoT based Smart Agricultural Applications0
The bottleneck and ceiling effects in quantized tracking control of heterogeneous multi-agent systems under DoS attacks0
Q-Learning for Stochastic Control under General Information Structures and Non-Markovian Environments0
Exploring Post-Training Quantization of Protein Language ModelsCode0
A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification0
Resource Constrained Semantic Segmentation for Waste SortingCode0
Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications0
QWID: Quantized Weed Identification Deep neural networkCode0
Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation0
High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise0
NIF: A Fast Implicit Image Compression with Bottleneck Layers and Modulated Sinusoidal ActivationsCode0
Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation0
ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers0
Deep Imbalanced Regression via Hierarchical Classification Adjustment0
General Point Model with Autoencoding and Autoregressive0
Transmitting Data Through Reconfigurable Intelligent Surface: A Spatial Sigma-Delta Modulation Approach0
Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte CarloCode0
Wide Flat Minimum Watermarking for Robust Ownership Verification of GANs0
LDPC Decoding with Degree-Specific Neural Message Weights and RCQ Decoding0
Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph RepresentationCode0
Federated learning compression designed for lightweight communicationsCode0
VQ-NeRF: Vector Quantization Enhances Implicit Neural Representations0
Deep Autoencoder-based Z-Interference Channels with Perfect and Imperfect CSI0
Spatial Sigma-Delta Modulation for Coarsely Quantized Massive MIMO Downlink: Flexible Designs by Convex Optimization0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17Unverified
8Xception W8A8Top-1 Accuracy (%)78.97Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04Unverified
2DTQMAP0.79Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3Unverified
2OutEffHop-Bert_basePerplexity6.21Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8Unverified