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 2651–2700 of 4925 papers

TitleStatusHype
CSQ: Centered Symmetric Quantization for Extremely Low Bit Neural Networks—0
CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification—0
CSR:Achieving 1 Bit Key-Value Cache via Sparse Representation—0
CTMQ: Cyclic Training of Convolutional Neural Networks with Multiple Quantization Steps—0
CURSOR-BASED ADAPTIVE QUANTIZATION FOR DEEP NEURAL NETWORK—0
Curvature in the Looking-Glass: Optimal Methods to Exploit Curvature of Expectation in the Loss Landscape—0
Custom Gradient Estimators are Straight-Through Estimators in Disguise—0
D^2MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving—0
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning—0
DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation—0
DART: Disentanglement of Accent and Speaker Representation in Multispeaker Text-to-Speech—0
DASNet: Dynamic Activation Sparsity for Neural Network Efficiency Improvement—0
Data Augmentations in Deep Weight Spaces—0
Data Clustering using a Hybrid of Fuzzy C-Means and Quantum-behaved Particle Swarm Optimization—0
Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI—0
Data-Driven Depth Map Refinement via Multi-Scale Sparse Representation—0
Data-driven Dynamic Event-triggered Control—0
Data-Driven Sparsity-Based Restoration of JPEG-Compressed Images in Dual Transform-Pixel Domain—0
Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks—0
Data-Free Group-Wise Fully Quantized Winograd Convolution via Learnable Scales—0
Data-free mixed-precision quantization using novel sensitivity metric—0
Data-Free Network Compression via Parametric Non-Uniform Mixed Precision Quantization—0
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning—0
Data-Free Quantization via Pseudo-label Filtering—0
Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization—0
Data-freeWeight Compress and Denoise for Large Language Models—0
Data Quality-aware Mixed-precision Quantization via Hybrid Reinforcement Learning—0
Dataset Distillation as Pushforward Optimal Quantization—0
DB-LLM: Accurate Dual-Binarization for Efficient LLMs—0
DBQ: A Differentiable Branch Quantizer for Lightweight Deep Neural Networks—0
DCNGAN: A Deformable Convolutional-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video—0
DC-PCN: Point Cloud Completion Network with Dual-Codebook Guided Quantization—0
Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers—0
Decentralized Low-Rank Fine-Tuning of Large Language Models—0
Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking—0
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression—0
Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval—0
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes—0
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning—0
DEED: A General Quantization Scheme for Communication Efficiency in Bits—0
Deep activity propagation via weight initialization in spiking neural networks—0
Deep and Shallow Covariance Feature Quantization for 3D Facial Expression Recognition—0
Deep Asymmetric Hashing with Dual Semantic Regression and Class Structure Quantization—0
Deep Attentive Generative Adversarial Network for Photo-Realistic Image De-Quantization—0
Deep Autoencoder-based Z-Interference Channels with Perfect and Imperfect CSI—0
Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes—0
Deep Conditional Measure Quantization—0
Deep Convolutional Compression for Massive MIMO CSI Feedback—0
Deep data compression for approximate ultrasonic image formation—0
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks—0
Show:102550
← PrevPage 54 of 99Next →
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