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

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Showing 951–1000 of 4925 papers

TitleStatusHype
A Survey of Quantization Methods for Efficient Neural Network Inference—0
Constrained Approximate Similarity Search on Proximity Graph—0
CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks—0
A Survey of Model Compression and Acceleration for Deep Neural Networks—0
A flexible, extensible software framework for model compression based on the LC algorithm—0
A Survey of Methods for Low-Power Deep Learning and Computer Vision—0
Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization—0
Constructing High-Order Signed Distance Maps from Computed Tomography Data with Application to Bone Morphometry—0
Conditionally Deep Hybrid Neural Networks Across Edge and Cloud—0
A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms—0
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression—0
Contextual Compression Encoding for Large Language Models: A Novel Framework for Multi-Layered Parameter Space Pruning—0
A Survey on Deep Hashing Methods—0
A Formalization of Image Vectorization by Region Merging—0
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence—0
Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G—0
Conditional Distribution Quantization in Machine Learning—0
A Study on Unsupervised Dictionary Learning and Feature Encoding for Action Classification—0
A study on speech enhancement using exponent-only floating point quantized neural network (EOFP-QNN)—0
Continuous Control with Action Quantization from Demonstrations—0
Continuous Speech Synthesis using per-token Latent Diffusion—0
A Survey on Learning to Hash—0
A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks—0
Contrastive Mutual Information Maximization for Binary Neural Networks—0
CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation—0
Contrastive Quant: Quantization Makes Stronger Contrastive Learning—0
Convergence of Federated Learning over a Noisy Downlink—0
Convergence rate of sign stochastic gradient descent for non-convex functions—0
Computing with Hypervectors for Efficient Speaker Identification—0
Convergence Rates for Regularized Optimal Transport via Quantization—0
Convergence Theory of Generalized Distributed Subgradient Method with Random Quantization—0
AsymKV: Enabling 1-Bit Quantization of KV Cache with Layer-Wise Asymmetric Quantization Configurations—0
Compute-Optimal LLMs Provably Generalize Better With Scale—0
How Does Batch Normalization Help Binary Training?—0
Computation-Efficient Quantization Method for Deep Neural Networks—0
Convolutional neural network compression for natural language processing—0
Convolutional Neural Network Quantization using Generalized Gamma Distribution—0
Convolutional Neural Networks Quantization with Attention—0
Asymmetric Learning Vector Quantization for Efficient Nearest Neighbor Classification in Dynamic Time Warping Spaces—0
Aggregated Learning: A Deep Learning Framework Based on Information-Bottleneck Vector Quantization—0
Coordinated Per-Antenna Power Minimization for Multicell Massive MIMO Systems with Low-Resolution Data Converters—0
CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness—0
Coreset-Based Neural Network Compression—0
Asymptotic Analysis of One-bit Quantized Box-Constrained Precoding in Large-Scale Multi-User Systems—0
Correlated quantization for distributed mean estimation and optimization—0
Correlated Quantization for Faster Nonconvex Distributed Optimization—0
Correlation Hashing Network for Efficient Cross-Modal Retrieval—0
CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization—0
Cost-Aware Routing for Efficient Text-To-Image Generation—0
A Structurally Regularized Convolutional Neural Network for Image Classification using Wavelet-based SubBand Decomposition—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