Deep Learning for Generic Object Detection: A Survey
paper : https://arxiv.org/abs/1809.02165v1
#computervision #deeplearning #patternrecognition @ArtificialIntelligenceArticles
paper : https://arxiv.org/abs/1809.02165v1
#computervision #deeplearning #patternrecognition @ArtificialIntelligenceArticles
Do Better ImageNet Models Transfer Better?
By Simon Kornblith, Jonathon Shlens, Quoc V. Le: https://arxiv.org/abs/1805.08974
#ComputerVision #PatternRecognition #MachineLearning
By Simon Kornblith, Jonathon Shlens, Quoc V. Le: https://arxiv.org/abs/1805.08974
#ComputerVision #PatternRecognition #MachineLearning
SlowFast Networks for Video Recognition
Feichtenhofer et al.: https://arxiv.org/abs/1812.03982
#MachineLearning #ComputerVision #DeepLearning #PatternRecognition #Technology
Feichtenhofer et al.: https://arxiv.org/abs/1812.03982
#MachineLearning #ComputerVision #DeepLearning #PatternRecognition #Technology
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SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
Jo and Park: https://arxiv.org/abs/1902.06838f
GitHub: https://github.com/JoYoungjoo/SC-FEGAN
#ComputerVision #GenerativeAdversarialNetwork #PatternRecognition
Jo and Park: https://arxiv.org/abs/1902.06838f
GitHub: https://github.com/JoYoungjoo/SC-FEGAN
#ComputerVision #GenerativeAdversarialNetwork #PatternRecognition
Reversible Adversarial Examples
Liu et al.: https://arxiv.org/abs/1811.00189
#ComputerVision #PatternRecognition #MachineLearning #MontréalAI #NeuralNetworks
Liu et al.: https://arxiv.org/abs/1811.00189
#ComputerVision #PatternRecognition #MachineLearning #MontréalAI #NeuralNetworks
Machine learning and artificial intelligence in the quantum domain"
By Vedran Dunjko, Hans J. Briegel: https://arxiv.org/abs/1709.02779
#QuantumPhysics #ArtificialIntelligence #ComputerVision #MontrealAI #PatternRecognition
By Vedran Dunjko, Hans J. Briegel: https://arxiv.org/abs/1709.02779
#QuantumPhysics #ArtificialIntelligence #ComputerVision #MontrealAI #PatternRecognition
This one is a must read - the latest #ComputerVision #PatternRecognition https://deepai.org/publication/relational-action-forecasting
Meta-Sim: Learning to Generate Synthetic Datasets
Kar et al.: https://arxiv.org/abs/1904.11621 @ArtificialIntelligenceArticles
#ComputerVision #PatternRecognition #ArtificialIntelligence
Kar et al.: https://arxiv.org/abs/1904.11621 @ArtificialIntelligenceArticles
#ComputerVision #PatternRecognition #ArtificialIntelligence
Fast AutoAugment
Lim et al.:
https://arxiv.org/abs/1905.00397
Code:
https://github.com/KakaoBrain/fast-autoaugment
#MachineLearning #ComputerVision #PatternRecognition
Lim et al.:
https://arxiv.org/abs/1905.00397
Code:
https://github.com/KakaoBrain/fast-autoaugment
#MachineLearning #ComputerVision #PatternRecognition
arXiv.org
Fast AutoAugment
Data augmentation is an essential technique for improving generalization ability of deep learning models. Recently, AutoAugment has been proposed as an algorithm to automatically search for...
Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
Schmidt et al.: https://arxiv.org/abs/1905.03709
#ComputerVision #PatternRecognition #ArtificialIntelligence
Schmidt et al.: https://arxiv.org/abs/1905.03709
#ComputerVision #PatternRecognition #ArtificialIntelligence
arXiv.org
Visualizing the Consequences of Climate Change Using...
We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs). By training our...