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 4501–4525 of 4925 papers

TitleStatusHype
Coreset-Based Neural Network Compression—0
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth PredictionCode0
Statistical Model Compression for Small-Footprint Natural Language Understanding—0
Hybrid Scene Compression for Visual Localization—0
Performance, Power, and Area Design Trade-offs in Millimeter-Wave Transmitter Beamforming ArchitecturesCode0
Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions—0
Accuracy to Throughput Trade-offs for Reduced Precision Neural Networks on Reconfigurable Logic—0
Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN)—0
Learning Product Codebooks using Vector Quantized Autoencoders for Image Retrieval—0
FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAsCode0
Deep attention-based classification network for robust depth predictionCode0
Two-stage iterative Procrustes match algorithm and its application for VQ-based speaker verification—0
Learning a Single Tucker Decomposition Network for Lossy Image Compression with Multiple Bits-Per-Pixel Rates—0
Deep Saliency Hashing—0
SYQ: Learning Symmetric Quantization For Efficient Deep Neural NetworksCode0
OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU—0
An Exact Quantized Decentralized Gradient Descent Algorithm—0
Convolutional Neural Networks to Enhance Coded SpeechCode0
Segmentation of Overlapped Steatosis in Whole-Slide Liver Histopathology Microscopy Images—0
Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Summation—0
Virtual Codec Supervised Re-Sampling Network for Image Compression—0
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization—0
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices—0
Quantizing deep convolutional networks for efficient inference: A whitepaperCode0
Parcels of Universe or why Schr\"odinger and Fourier are so relatives?—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