Detection Free Human Instance Segmentation using Pose2Seg and PyTorch
https://towardsdatascience.com/detection-free-human-instance-segmentation-using-pose2seg-and-pytorch-72f48dc4d23e
https://towardsdatascience.com/detection-free-human-instance-segmentation-using-pose2seg-and-pytorch-72f48dc4d23e
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Article: https://arxiv.org/pdf/1703.10593.pdf
PyTorch Code: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
Article: https://arxiv.org/pdf/1703.10593.pdf
PyTorch Code: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
GitHub
GitHub - junyanz/pytorch-CycleGAN-and-pix2pix: Image-to-Image Translation in PyTorch
Image-to-Image Translation in PyTorch. Contribute to junyanz/pytorch-CycleGAN-and-pix2pix development by creating an account on GitHub.
25 Excellent Machine Learning Open Datasets
https://opendatascience.com/25-excellent-machine-learning-open-datasets/
https://opendatascience.com/25-excellent-machine-learning-open-datasets/
Open Data Science - Your News Source for AI, Machine Learning & more
25 Excellent Machine Learning Open Datasets
Looking to work on some data, but can't collect your own? Here are 25 helpful machine learning open datasets to use today!
A Gentle Introduction to Object Recognition With Deep Learning
https://machinelearningmastery.com/object-recognition-with-deep-learning/
https://machinelearningmastery.com/object-recognition-with-deep-learning/
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Video: youtu.be/p1b5aiTrGzY
Paper: arxiv.org/abs/1905.08233
Video: youtu.be/p1b5aiTrGzY
Paper: arxiv.org/abs/1905.08233
YouTube
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Statement regarding the purpose and effect of the technology
(NB: this statement reflects personal opinions of the authors and not of their organizations)
We believe that telepresence technologies in AR, VR and other media are to transform the world in the…
(NB: this statement reflects personal opinions of the authors and not of their organizations)
We believe that telepresence technologies in AR, VR and other media are to transform the world in the…
Data-Efficient Image Recognition with Contrastive Predictive Coding
Article: https://arxiv.org/abs/1905.09272
Article: https://arxiv.org/abs/1905.09272
arXiv.org
Data-Efficient Image Recognition with Contrastive Predictive Coding
Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient...
Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction
http://ai.googleblog.com/2019/05/moving-camera-moving-people-deep.html
http://ai.googleblog.com/2019/05/moving-camera-moving-people-deep.html
research.google
Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction
Posted by Tali Dekel, Research Scientist and Forrester Cole, Software Engineer, Machine Perception The human visual system has a remarkable abili...
How to Perform Object Detection in Photographs Using Mask R-CNN with Keras
https://machinelearningmastery.com/how-to-perform-object-detection-in-photographs-with-mask-r-cnn-in-keras/
https://machinelearningmastery.com/how-to-perform-object-detection-in-photographs-with-mask-r-cnn-in-keras/
TensorWatch: a debugging and visualization tool designed for deep learning
https://github.com/microsoft/tensorwatch
https://github.com/microsoft/tensorwatch
GitHub
GitHub - microsoft/tensorwatch: Debugging, monitoring and visualization for Python Machine Learning and Data Science
Debugging, monitoring and visualization for Python Machine Learning and Data Science - microsoft/tensorwatch
Estimators, Loss Functions, Optimizers —Core of ML Algorithms
https://towardsdatascience.com/estimators-loss-functions-optimizers-core-of-ml-algorithms-d603f6b0161a]
https://towardsdatascience.com/estimators-loss-functions-optimizers-core-of-ml-algorithms-d603f6b0161a]
Medium
Estimators, Loss Functions, Optimizers —Core of ML Algorithms
In order to understand how a machine learning algorithm learns from data to predict an outcome, it is essential to understand the…
Torchvision 0.3: segmentation, detection models, new datasets
https://pytorch.org/blog/torchvision03/
https://pytorch.org/blog/torchvision03/
COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration
https://arxiv.org/abs/1905.09275
https://arxiv.org/abs/1905.09275
illustrated Artificial Intelligence cheatsheets covering the content of the CS 221 class
Link: https://stanford.edu/~shervine/teaching/cs-221/
Reflex-based models with Machine Learning: https://stanford.edu/~shervine/teaching/cs-221/cheatsheet-reflex-models
Link: https://stanford.edu/~shervine/teaching/cs-221/
Reflex-based models with Machine Learning: https://stanford.edu/~shervine/teaching/cs-221/cheatsheet-reflex-models
stanford.edu
Teaching - CS 221
Teaching page of Shervine Amidi, Graduate Student at Stanford University.
How degenerate is the parametrization of neural networks with the ReLU activation function?
https://arxiv.org/abs/1905.09803
https://arxiv.org/abs/1905.09803
How to Perform Object Detection With YOLOv3 in Keras
https://machinelearningmastery.com/how-to-perform-object-detection-with-yolov3-in-keras/
https://machinelearningmastery.com/how-to-perform-object-detection-with-yolov3-in-keras/
MachineLearningMastery.com
How to Perform Object Detection With YOLOv3 in Keras - MachineLearningMastery.com
Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph. It is a challenging problem that involves building upon methods for object recognition (e.g. where are they)…
Forwarded from Artificial Intelligence
Unsupervised Learning with Graph Neural Networks
video: http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule
guide: http://helper.ipam.ucla.edu/publications/glws4/glws4_15546.pdf
video: http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule
guide: http://helper.ipam.ucla.edu/publications/glws4/glws4_15546.pdf
IPAM
Workshop IV: Deep Geometric Learning of Big Data and Applications - IPAM
Augmented Neural ODEs
Github: https://github.com/EmilienDupont/augmented-neural-odes
Article: https://arxiv.org/abs/1904.01681
Github: https://github.com/EmilienDupont/augmented-neural-odes
Article: https://arxiv.org/abs/1904.01681
GitHub
GitHub - EmilienDupont/augmented-neural-odes: Pytorch implementation of Augmented Neural ODEs :sunflower:
Pytorch implementation of Augmented Neural ODEs :sunflower: - EmilienDupont/augmented-neural-odes
SimpleSelfAttention
The purpose of this repository is two-fold:
-demonstrate improvements brought by the use of a self-attention layer in an image -classification model.
introduce a new layer which I call SimpleSelfAttention
https://github.com/sdoria/SimpleSelfAttention
The purpose of this repository is two-fold:
-demonstrate improvements brought by the use of a self-attention layer in an image -classification model.
introduce a new layer which I call SimpleSelfAttention
https://github.com/sdoria/SimpleSelfAttention
GitHub
GitHub - sdoria/SimpleSelfAttention: A simpler version of the self-attention layer from SAGAN, and some image classification results.
A simpler version of the self-attention layer from SAGAN, and some image classification results. - sdoria/SimpleSelfAttention