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 3876–3900 of 4925 papers

TitleStatusHype
Predicting Generalization in Deep Learning via Local Measures of Distortion—0
Predicting Multi-Codebook Vector Quantization Indexes for Knowledge Distillation—0
Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE—0
Latent-Domain Predictive Neural Speech Coding—0
Predictive Uncertainty through Quantization—0
PredToken: Predicting Unknown Tokens and Beyond with Coarse-to-Fine Iterative Decoding—0
Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization—0
Preliminary study on using vector quantization latent spaces for TTS/VC systems with consistent performance—0
Preprocessing Enhanced Image Compression for Machine Vision—0
PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models—0
Pre-Quantized Deep Learning Models Codified in ONNX to Enable Hardware/Software Co-Design—0
Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation—0
Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare—0
Privacy-Preserving Speech Representation Learning using Vector Quantization—0
Privacy-Preserving UCB Decision Process Verification via zk-SNARKs—0
Private LoRA Fine-tuning of Open-Source LLMs with Homomorphic Encryption—0
Prive-HD: Privacy-Preserved Hyperdimensional Computing—0
PrivQuant: Communication-Efficient Private Inference with Quantized Network/Protocol Co-Optimization—0
Probabilistically Sampled and Spectrally Clustered Plant Genotypes using Phenotypic Characteristics—0
Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices—0
Product Quantization Network for Fast Image Retrieval—0
Product Quantizer Aware Inverted Index for Scalable Nearest Neighbor Search—0
Product Split Trees—0
ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes—0
Progressive Compression with Universally Quantized Diffusion Models—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