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 3001–3025 of 4925 papers

TitleStatusHype
Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI—0
Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures—0
Tk-merge: Computationally Efficient Robust Clustering Under General Assumptions—0
Improving Performance of Semantic Segmentation CycleGANs by Noise Injection into the Latent Segmentation Space—0
VAQF: Fully Automatic Software-Hardware Co-Design Framework for Low-Bit Vision Transformer—0
Neural Network Compression of ACAS Xu Early Prototype is Unsafe: Closed-Loop Verification through Quantized State BackreachabilityCode0
UWC: Unit-wise Calibration Towards Rapid Network Compression—0
Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing—0
Boosted Dense Retriever—0
UDC: Unified DNAS for Compressible TinyML Models—0
ViT2Hash: Unsupervised Information-Preserving Hashing—0
Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based RetrievalCode1
A Planck Radiation and Quantization Scheme for Human Cognition and Language—0
A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent FlowCode0
Bandlimited signal reconstruction from leaky integrate-and-fire encoding using POCS—0
An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic—0
PocketNN: Integer-only Training and Inference of Neural Networks via Direct Feedback Alignment and Pocket Activations in Pure C++Code1
High-contrast, speckle-free, true 3D holography via binary CGH optimization—0
Video Coding for Machines: Partial transmission of SIFT features—0
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion TheoryCode0
Detection of extragalactic Ultra-Compact Dwarfs and Globular Clusters using Explainable AI techniquesCode0
A Robust Visual Sampling Model Inspired by Receptive Field—0
Elastic Product Quantization for Time SeriesCode0
IQDUBBING: Prosody modeling based on discrete self-supervised speech representation for expressive voice conversion—0
Quantization-Aware Deep Optics for Diffractive Snapshot Hyperspectral Imaging—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