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 1201–1250 of 4925 papers

TitleStatusHype
An efficient and straightforward online quantization method for a data stream through remove-birth updatingCode0
AdaBin: Improving Binary Neural Networks with Adaptive Binary SetsCode0
An Edge Computing-Based Solution for Real-Time Leaf Disease Classification using Thermal ImagingCode0
Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic LocomotionCode0
JPEG Inspired Deep LearningCode0
A Binary Variational Autoencoder for HashingCode0
Binarized Knowledge Graph EmbeddingsCode0
An asymmetric heuristic for trained ternary quantization based on the statistics of the weights: an application to medical signal classificationCode0
Bimodal Distributed Binarized Neural NetworksCode0
Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural NetworksCode0
Activations and Gradients Compression for Model-Parallel TrainingCode0
Joint Maximum Purity Forest with Application to Image Super-ResolutionCode0
Lipschitz Continuity Retained Binary Neural NetworkCode0
I&S-ViT: An Inclusive & Stable Method for Pushing the Limit of Post-Training ViTs QuantizationCode0
Iterative Training: Finding Binary Weight Deep Neural Networks with Layer BinarizationCode0
Is PGD-Adversarial Training Necessary? Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples OnlyCode0
BiFeat: Supercharge GNN Training via Graph Feature QuantizationCode0
Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language ModelsCode0
IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural NetworksCode0
Forward and Backward Information Retention for Accurate Binary Neural NetworksCode0
Integral Human Pose RegressionCode0
Activation Compression of Graph Neural Networks using Block-wise Quantization with Improved Variance MinimizationCode0
Integrated Encoding and Quantization to Enhance Quanvolutional Neural NetworksCode0
Integer-Only Neural Network Quantization Scheme Based on Shift-Batch-NormalizationCode0
Integer Quantization for Deep Learning Inference: Principles and Empirical EvaluationCode0
Beyond Product Quantization: Deep Progressive Quantization for Image RetrievalCode0
Instance-Aware Dynamic Neural Network QuantizationCode0
Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code MatchingCode0
Improving Neural Network Quantization without Retraining using Outlier Channel SplittingCode0
Improving Self-Supervised Learning-based MOS Prediction NetworksCode0
Improved Gradient based Adversarial Attacks for Quantized NetworksCode0
Digital and Hybrid Precoding Designs in Massive MIMO with Low-Resolution ADCsCode0
Implicit Feature Decoupling with Depthwise QuantizationCode0
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence ModelsCode0
Diffusion Models as Stochastic Quantization in Lattice Field TheoryCode0
Image Hashing by Minimizing Discrete Component-wise Wasserstein DistanceCode0
ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMsCode0
IBVC: Interpolation-driven B-frame Video CompressionCode0
A Bag-of-Words Equivalent Recurrent Neural Network for Action RecognitionCode0
Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance AnalysisCode0
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision WeightsCode0
Improved Knowledge Distillation for Crowd Counting on IoT DeviceCode0
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM CompressionCode0
Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural NetworksCode0
Hybrid coarse-fine classification for head pose estimationCode0
Differentiable Product Quantization for End-to-End Embedding CompressionCode0
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and MemoryCode0
Differentiable Product Quantization for Memory Efficient Camera RelocalizationCode0
HOT: Hadamard-based Optimized TrainingCode0
HyperFlow: Representing 3D Objects as SurfacesCode0
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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