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 401–425 of 4925 papers

TitleStatusHype
Generative Low-bitwidth Data Free QuantizationCode1
Compressing LLMs: The Truth is Rarely Pure and Never SimpleCode1
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural NetworksCode1
Deep Geometry Post-Processing for Decompressed Point CloudsCode1
Heatmap Regression via Randomized RoundingCode1
HHF: Hashing-guided Hinge Function for Deep Hashing RetrievalCode1
Hierarchical Vector Quantized Graph Autoencoder with Annealing-Based Code SelectionCode1
Deep Learning-Enabled One-Bit DoA EstimationCode1
FP8 Quantization: The Power of the ExponentCode1
FQ-ViT: Post-Training Quantization for Fully Quantized Vision TransformerCode1
HiNeRV: Video Compression with Hierarchical Encoding-based Neural RepresentationCode1
Comprehensive Graph-conditional Similarity Preserving Network for Unsupervised Cross-modal HashingCode1
Anonymizing Speech: Evaluating and Designing Speaker Anonymization TechniquesCode1
Deep PeNSieve: A deep learning framework based on the posit number systemCode1
1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bitCode1
Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical SystemsCode1
FOX-NAS: Fast, On-device and Explainable Neural Architecture SearchCode1
AQD: Towards Accurate Fully-Quantized Object DetectionCode1
Compress Any Segment Anything Model (SAM)Code1
Adaptive Debanding FilterCode1
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two QuantizationCode1
FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning SimulationsCode1
FP4 All the Way: Fully Quantized Training of LLMsCode1
FracBits: Mixed Precision Quantization via Fractional Bit-WidthsCode1
Compact representations of convolutional neural networks via weight pruning and quantizationCode1
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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