locussam / ai-reading

Articles

The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3) https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html

Clarifai launches SDK for training AI on your iPhone https://venturebeat.com/2017/07/12/clarifai-launches-sdk-for-running-ai-on-your-iphone/

Basics

Visualizing and Understanding Convolutional Networks http://www.matthewzeiler.com/pubs/arxive2013/arxive2013.pdf

Rectified Linear Units Improve Restricted Boltzmann Machines http://www.cs.toronto.edu/~fritz/absps/reluICML.pdf

Artistic Style

A Neural Algorithm of Artistic Style Leon A. Gatys, Alexander S. Ecker, Matthias Bethge https://arxiv.org/pdf/1508.06576v1.pdf "To generate the images that mix the content of a photograph with the style of a painting (Fig 2) we jointly minimise the distance of a white noise image from the content representation of the photograph in one layer of the network and the style representation of the painting in a number of layers of the CNN."

Texture Synthesis Using Convolutional Neural Networks https://arxiv.org/abs/1505.07376

AlexNet

ImageNet Classification with Deep Convolutional Neural Networks https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

VGG-19

Very Deep Convolutional Networks for Large-Scale Image Recognition https://arxiv.org/abs/1409.1556

SqueezeNet

SQUEEZENET: ALEXNET-LEVEL ACCURACY WITH 50X FEWER PARAMETERS AND <0.5MB MODEL SIZE https://arxiv.org/pdf/1602.07360.pdf

A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN https://blog.athelas.com/a-brief-history-of-cnns-in-image-segmentation-from-r-cnn-to-mask-r-cnn-34ea83205de4

MobileNet

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications https://arxiv.org/abs/1704.04861v1

Edge detection

Fast Edge Detection Using Structured Forests https://pdollar.github.io/files/papers/DollarPAMI15edges.pdf

Motion

Emergence of Locomotion Behaviours in Rich Environments https://arxiv.org/abs/1707.02286

Generative Adversarial Network

Generative adversarial networks https://arxiv.org/pdf/1406.2661.pdf

https://arxiv.org/pdf/1701.00160.pdf

Driving

Detecting Drivable Area for Self-driving Cars: An Unsupervised Approach https://arxiv.org/pdf/1705.00451.pdf

NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles https://arxiv.org/abs/1707.03501

Robust Adversarial Examples https://blog.openai.com/robust-adversarial-inputs/

Object Detection

Real-time object detection with YOLO http://machinethink.net/blog/object-detection-with-yolo/

YOYO https://arxiv.org/abs/1612.08242

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks https://arxiv.org/abs/1506.01497

Selective Search for Object Recognition http://www.cs.cornell.edu/courses/cs7670/2014sp/slides/VisionSeminar14.pdf

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks https://arxiv.org/abs/1506.01497

Mask R-CNN https://arxiv.org/abs/1703.06870

Pascal VOC (Visual Object Classes) http://host.robots.ox.ac.uk/pascal/VOC/

Visual Interaction

Visual Interaction Networks https://arxiv.org/abs/1706.01433

Tracking

Toward Geometric Deep SLAM https://arxiv.org/pdf/1707.07410.pdf

Why is ARKit better than the alternatives? https://medium.com/super-ventures-blog/why-is-arkit-better-than-the-alternatives-af8871889d6a

ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes https://arxiv.org/abs/1702.04405

Federated Training

Communication-Efficient Learning of Deep Networks from Decentralized Data https://arxiv.org/pdf/1602.05629.pdf

Fine Tuning

A Comprehensive guide to Fine-tuning Deep Learning Models in Keras (Part I) https://flyyufelix.github.io/2016/10/03/fine-tuning-in-keras-part1.html