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 4651–4675 of 4925 papers

TitleStatusHype
Compressing Word Embeddings via Deep Compositional Code LearningCode0
SUT System Description for Anti-Spoofing 2017 Challenge—0
Efficient Inferencing of Compressed Deep Neural Networks—0
Towards Effective Low-bitwidth Convolutional Neural NetworksCode0
Attacking Binarized Neural Networks—0
Quantization goes Polynomial—0
Deep Hashing with Triplet Quantization Loss—0
Deep Learning as a Mixed Convex-Combinatorial Optimization ProblemCode0
Using the quantization error from Self-Organized Map (SOM) output for detecting critical variability in large bodies of image time series in less than a minute—0
End-to-End Optimized Speech Coding with Deep Neural Networks—0
High Five: Improving Gesture Recognition by Embracing Uncertainty—0
A Survey of Model Compression and Acceleration for Deep Neural Networks—0
Low Precision RNNs: Quantizing RNNs Without Losing Accuracy—0
Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression—0
TensorQuant - A Simulation Toolbox for Deep Neural Network QuantizationCode0
STDP Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy Efficient Recognition—0
Quantized Minimum Error Entropy Criterion—0
3D Surface Detail Enhancement From a Single Normal Map—0
Compressive Quantization for Fast Object Instance Search in Videos—0
AnnArbor: Approximate Nearest Neighbors Using Arborescence Coding—0
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantizationCode0
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks—0
E^2BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network—0
Simulated Annealing for JPEG Quantization—0
Towards Compact and Fast Neural Machine Translation Using a Combined Method—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