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 2076–2100 of 4925 papers

TitleStatusHype
Weakly Supervised Deep Hyperspherical Quantization for Image RetrievalCode0
Nanometer Scanning with Micrometer Sensing: Beating Quantization Constraints in Lissajous Trajectory Tracking—0
What Happens When Small Is Made Smaller? Exploring the Impact of Compression on Small Data Pretrained Language Models—0
Fine-Tuning, Quantization, and LLMs: Navigating Unintended Outcomes—0
TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices—0
Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation RegularizationCode0
DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement—0
CLaM-TTS: Improving Neural Codec Language Model for Zero-Shot Text-to-Speech—0
Cherry on Top: Parameter Heterogeneity and Quantization in Large Language Models—0
DNN Memory Footprint Reduction via Post-Training Intra-Layer Multi-Precision Quantization—0
NeRFCodec: Neural Feature Compression Meets Neural Radiance Fields for Memory-Efficient Scene Representation—0
Minimize Quantization Output Error with Bias CompensationCode0
On the Effect of Quantization on Dynamic Mode Decomposition—0
RefQSR: Reference-based Quantization for Image Super-Resolution Networks—0
A Novel Audio Representation for Music Genre Identification in MIR—0
Instance-Aware Group Quantization for Vision Transformers—0
Towards Variable and Coordinated Holistic Co-Speech Motion Generation—0
Accurate Block Quantization in LLMs with Outliers—0
Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs—0
QNCD: Quantization Noise Correction for Diffusion ModelsCode0
Meta-Heuristic Fronthaul Bit Allocation for Cell-free Massive MIMO Systems—0
Uncertainty-Aware Deep Video Compression with Ensembles—0
Within the Dynamic Context: Inertia-aware 3D Human Modeling with Pose Sequence—0
Oh! We Freeze: Improving Quantized Knowledge Distillation via Signal Propagation Analysis for Large Language Models—0
Order of Compression: A Systematic and Optimal Sequence to Combinationally Compress CNN—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