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 4476–4500 of 4925 papers

TitleStatusHype
Deep reverse tone mappingCode0
Autoregressive Co-Training for Learning Discrete Speech RepresentationsCode0
Quantized Prompt for Efficient Generalization of Vision-Language ModelsCode0
A Binary Variational Autoencoder for HashingCode0
An Information-Theoretic Analysis of Self-supervised Discrete Representations of SpeechCode0
Semi-supervised 3D Object Detection with PatchTeacher and PillarMixCode0
Adaptive Computation Modules: Granular Conditional Computation For Efficient InferenceCode0
Light Multi-segment Activation for Model CompressionCode0
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement LearningCode0
Communication-Efficient Federated Learning via Predictive CodingCode0
Algorithm and VLSI Design for 1-bit Data Detection in Massive MIMO-OFDMCode0
LiFT: Lightweight, FPGA-tailored 3D object detection based on LiDAR dataCode0
LFZip: Lossy compression of multivariate floating-point time series data via improved predictionCode0
Expansion Quantization Network: An Efficient Micro-emotion Annotation and Detection FrameworkCode0
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation AnalysisCode0
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range ContentCode0
SYQ: Learning Symmetric Quantization For Efficient Deep Neural NetworksCode0
One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language ModelsCode0
One-Step Forward and Backtrack: Overcoming Zig-Zagging in Loss-Aware Quantization TrainingCode0
EXAQ: Exponent Aware Quantization For LLMs AccelerationCode0
Leveraging Continuously Differentiable Activation Functions for Learning in Quantized Noisy EnvironmentsCode0
Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI FeedbackCode0
An exploration of the effect of quantisation on energy consumption and inference time of StarCoder2Code0
Towards Highly Accurate and Stable Face Alignment for High-Resolution VideosCode0
Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized ModelsCode0
Show:102550
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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