Data-Efficient Image Recognition with Contrastive Predictive Coding
Hénaff et al.: https://arxiv.org/abs/1905.09272
#ArtificialIntelligence #ComputerVision #MachineLearning
Hénaff et al.: https://arxiv.org/abs/1905.09272
#ArtificialIntelligence #ComputerVision #MachineLearning
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Zakharov et al.: https://arxiv.org/abs/1905.08233
#ComputerVision #GenerativeAdversarialNetworks #MachineLearning
Zakharov et al.: https://arxiv.org/abs/1905.08233
#ComputerVision #GenerativeAdversarialNetworks #MachineLearning
arXiv.org
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Several recent works have shown how highly realistic human head images can be obtained by training convolutional neural networks to generate them. In order to create a personalized talking head...
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Zakharov et al.: https://arxiv.org/abs/1905.08233
@ArtificialIntelligenceArticles
#ComputerVision #GenerativeAdversarialNetworks #MachineLearning
Zakharov et al.: https://arxiv.org/abs/1905.08233
@ArtificialIntelligenceArticles
#ComputerVision #GenerativeAdversarialNetworks #MachineLearning
Week 2 CS294-158 Deep Unsupervised Learning (2/6/19)
#UCBerkeley #deeplearning #computervision
https://m.youtube.com/watch?v=mYCLVPRy2nc&feature=share
#UCBerkeley #deeplearning #computervision
https://m.youtube.com/watch?v=mYCLVPRy2nc&feature=share
YouTube
Week 2 CS294-158 Deep Unsupervised Learning (2/6/19)
UC Berkeley CS294-158 Deep Unsupervised Learning (Spring 2019)
Instructors: Pieter Abbeel, Xi (Peter) Chen, Jonathan Ho, Aravind Srinivas
https://sites.google.com/view/berkeley-cs294-158-sp19/home
Week 2 Lecture Contents:
- Likelihood Models Part I: Autoregressive…
Instructors: Pieter Abbeel, Xi (Peter) Chen, Jonathan Ho, Aravind Srinivas
https://sites.google.com/view/berkeley-cs294-158-sp19/home
Week 2 Lecture Contents:
- Likelihood Models Part I: Autoregressive…
Robustness beyond Security: Computer Vision Applications
Engstrom et al.: http://gradientscience.org/robust_apps/
#artificialintelligence #computervision #security #technology
Engstrom et al.: http://gradientscience.org/robust_apps/
#artificialintelligence #computervision #security #technology
gradient science
Robustness Beyond Security: Computer Vision Applications
An off-the-shelf robust classifier can be used to perform a range of computer vision tasks beyond classification.
The complete list of all 519 ICML-2019 papers with code. #icml2019 #AI #MachineLearning #ComputerVision #code
https://www.paperdigest.org/2019/05/icml-2019-papers-with-code/
https://www.paperdigest.org/2019/05/icml-2019-papers-with-code/
A key conference quality indicator is low paper acceptance rates. The CVPR 2019 paper acceptance rate dropped to 25.1 percent from last year’s 29.6 percent 🤓☝️
The list of all 1300 research papers accepted for CVPR 2019 is available here: http://openaccess.thecvf.com/CVPR2019.py
Given you spend 1 hour to read 1 article and the rate of 8 articles per day, it will take you about 6 months to read all of them. You'd better start right now 🙃
#CVPR2019 #computervision #patternrecognition #deeplearning #machinelearning
The list of all 1300 research papers accepted for CVPR 2019 is available here: http://openaccess.thecvf.com/CVPR2019.py
Given you spend 1 hour to read 1 article and the rate of 8 articles per day, it will take you about 6 months to read all of them. You'd better start right now 🙃
#CVPR2019 #computervision #patternrecognition #deeplearning #machinelearning
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
Simon et al.: https://arxiv.org/abs/1803.06199
#ArtificialIntelligence #ComputerVision #DeepLearning #MachineLearning #PatternRecognition
Simon et al.: https://arxiv.org/abs/1803.06199
#ArtificialIntelligence #ComputerVision #DeepLearning #MachineLearning #PatternRecognition
Adaptive Neural Trees
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
arXiv.org
Adaptive Neural Trees
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is...
Learning Data Augmentation Strategies for Object Detection
Zoph et al.: https://arxiv.org/abs/1906.11172
#ArtificialIntelligence #ComputerVision #PatternRecognition
Zoph et al.: https://arxiv.org/abs/1906.11172
#ArtificialIntelligence #ComputerVision #PatternRecognition
arXiv.org
Learning Data Augmentation Strategies for Object Detection
Data augmentation is a critical component of training deep learning models. Although data augmentation has been shown to significantly improve image classification, its potential has not been...
Adaptive Neural Trees
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
arXiv.org
Adaptive Neural Trees
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is...
Deep Learning and Medical Imaging: Part 2 🎯]
If you're a crafty AI engineer who wants to play with code to learn how things work, just keep reading !
In this post, you'll learn how to use PyTorch to train an Anterior Ligament Cruciate tear classifier that successfully detects these injuries from the MRNet MRI dataset with a very high performance (AUC > 0.95)
You'll dive into the code and go through various tips and tricks ranging from transfer learning to data augmentation, stacking and handling medical images.
You'll also learn about optimization tricks as well as how to organize code efficiently with neural architecture design.
Link to part 2: https://ahmedbesbes.com/automate-the-diagnosis-of-knee-injuries-with-deep-learning-part-2-building-an-acl-tear-classifier.html
Github repo with full code: https://github.com/ahmedbesbes/mrnet
#deeplearning #mediclaimaging #computervision
If you're a crafty AI engineer who wants to play with code to learn how things work, just keep reading !
In this post, you'll learn how to use PyTorch to train an Anterior Ligament Cruciate tear classifier that successfully detects these injuries from the MRNet MRI dataset with a very high performance (AUC > 0.95)
You'll dive into the code and go through various tips and tricks ranging from transfer learning to data augmentation, stacking and handling medical images.
You'll also learn about optimization tricks as well as how to organize code efficiently with neural architecture design.
Link to part 2: https://ahmedbesbes.com/automate-the-diagnosis-of-knee-injuries-with-deep-learning-part-2-building-an-acl-tear-classifier.html
Github repo with full code: https://github.com/ahmedbesbes/mrnet
#deeplearning #mediclaimaging #computervision
Ahmed BESBES - Data Science Portfolio
Automate the diagnosis of Knee Injuries with Deep Learning part 2: Building an ACL tear classifier
In this post, you'll build up on the intuitions you gathered on MRNet data by following the previous post. You'll learn how to use PyTorch to train an ACL tear classifier that sucessfully detects these injuries from MRIs with a very high performance. We'll…
Natural Adversarial Examples
Hendrycks et al.: https://arxiv.org/abs/1907.07174) arxiv.org/abs/1907.07174
Dataset and code: https://github.com/hendrycks/natural-adv-examples
#MachineLearning #ComputerVision #PatternRecognition
Hendrycks et al.: https://arxiv.org/abs/1907.07174) arxiv.org/abs/1907.07174
Dataset and code: https://github.com/hendrycks/natural-adv-examples
#MachineLearning #ComputerVision #PatternRecognition
arXiv.org
Natural Adversarial Examples
We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtration technique to...