"Video Imagination from a Single Image with Transformation Generation": https://arxiv.org/abs/1706.04124 Code: https://github.com/gitpub327/VideoImagination

Strong results for action recognition by transferring Inception weights of ImageNet to video
https://arxiv.org/abs/1705.07750

deeplearning For Image Recognition : Deep Learni here https://zpy.io/e254fa70 #deeplearning

Navigating the #AI #ethical minefield without getting blown up

https://bit.ly/2tfVByU #fintech #BigData #IoT #machinelearning

Bat Detective - #DeepLearning Tools for Bat Acoustic Signal Detection.
https://buff.ly/2unYgF0 #BigData #MachineLearning #DataScience #AI

DeepMind expands to Canada with new research office in Edmonton, Alberta https://deepmind.com/blog/deepmind-office-canada-edmonton/

#AI is changing how we do #science. Get a glimpse

#ML #DataScience #BigData
#fintech #Insurtech
https://bit.ly/2uqdtp9

Stanford's Convolutional #NeuralNetworks for #Visual Recognition (CS231n): Course Projects Spring 2017 https://buff.ly/2tNXvIB

A 2017 Guide to Semantic Segmentation with Deep Learning
#AI #MachineLearning #DeepLearning #ML #DL #tech
https://blog.qure.ai/notes/semantic-segmentation-deep-learning-review

"Adversarial Representation Learning for Domain Adaptation": Wasserstein DANN...? https://arxiv.org/abs/1707.01217

Variance Regularizing Adversarial Learning" by MILA: meta-adversarial training for bi-modal distribution match https://arxiv.org/abs/1707.00309

Skeleton-aided Articulated Motion Generation: based on GAN rather than LSTM https://arxiv.org/abs/1707.01058

Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules": CNN+GAP+various scale https://arxiv.org/abs/1707.01086

Improving Content-Invariance in Gated Autoencoders for 2D and 3D Object Rotation": learning relations b/w instances https://arxiv.org/abs/1707.01357

Teacher-Student Curriculum Learning" by OpenAI, https://arxiv.org/abs/1707.00183

Advances in Deep Neural Networks," at ACM Turing 50 Celebration https://www.youtube.com/watch?v=mFYM9j8bGtg&feature=youtu.be

Deep Learning: A Practitioner's Approach here https://zpy.io/0463bcb8 #deeplearning

Exploring the structure of a real-time, arbitrary neural artistic stylization network. https://arxiv.org/abs/1705.06830

New work showing a principled way to learn geometry and semantics from a single deep learning model https://arxiv.org/pdf/1705.07115.pdf

GeneGAN: "Learning Object Transfiguration & Attribute Subspace from Unpaired Data": https://arxiv.org/abs/1705.04932v1 Code: https://github.com/Prinsphield/GeneGAN

Interpreting the black box: Interesting paper + code quantifies and examines interpretability of CNN architectures. https://netdissect.csail.mit.edu/