Attentive Generative Adversarial Network for Raindrop Removal from A Single Image

Abstract : "Raindrops adhered to a glass window or camera lens can severely hamper the visibility of a background scene and degrade an image considerably. In this paper, we address the problem by visually removing raindrops, and thus transforming a raindrop degraded image into a clean one. The problem is intractable, since first the regions occluded by raindrops are not given. Second, the information about the background scene of the occluded regions is completely lost for most part. To resolve the problem, we apply an attentive generative network using adversarial training (...)."

Qian et al.: https://arxiv.org/pdf/1711.10098.pdf

#artificialintelligence #deeplearning #generativeadversarialnetwork

Artificial intelligence won’t revolutionize anything if hackers can mess with it.

https://www.technologyreview.com/s/613170/emtech-digital-dawn-song-adversarial-machine-learning/?utm_source=facebook&utm_medium=tr_social&utm_campaign=site_visitor.unpaid.engagement

Current machine-learning systems cannot generate their own optical illusions—at least not yet.

https://www.technologyreview.com/s/612261/neural-networks-dont-understand-what-optical-illusions-are/?utm_campaign=owned_social&utm_medium=social&utm_source=facebook.com

Collection of pretrained models for PyTorch
https://github.com/rwightman/pytorch-image-models

"Gauge Equivariant Convolutional Networks and the Icosahedral CNN
" pretty interesting way of thinking, i like that
https://arxiv.org/pdf/1902.04615.pdf

Model optimization with new Tensorflow tool
https://medium.com/tensorflow/tensorflow-model-optimization-toolkit-pruning-api-42cac9157a6a

Brain Network Mechanisms of General Intelligence
https://www.biorxiv.org/content/biorxiv/early/2019/06/03/657205.full.pdf

Randomly wired neural networks and state-of-the-art accuracy? Yes it works.
https://towardsdatascience.com/randomly-wired-neural-networks-and-state-of-the-art-accuracy-yes-it-works-9fb3cedc8059

Robustness beyond Security: Representation Learning
https://gradientscience.org/robust_reps/

Reprojection Losses: Deep Learning Surpassing Classical Geometry in Computer Vision?
https://alexgkendall.com/computer_vision/Reprojection_losses_geometry_computer_vision/

Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment.
https://github.com/videoflow/videoflow

Natural Language Inference with Deep Learning (NAACL 2019 Tutorial)

the slides for the 2019 NAACL tutorial on Natural Language Inference with Deep Learning
by Sam Bowman and Xiaodan Zhu.

https://nlitutorial.github.io/nli_tutorial.pdf

Robotic Psychology What Do We Know about Human-Robot Interaction and What Do We Still Need to Learn?
https://scholarspace.manoa.hawaii.edu/bitstream/10125/59633/0193.pdf

RF-Net: An End-to-End Image Matching Network based on Receptive Field
Shen et al.: https://arxiv.org/abs/1906.00604
#ArtificialIntelligence #DeepLearning #MachineLearning

Generating Diverse High-Fidelity Images with VQ-VAE-2
Razavi et al.: https://arxiv.org/abs/1906.00446
#ArtificialIntelligence #DeepLearning #MachineLearning

A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).

https://github.com/benedekrozemberczki/graph2vec

A Survival Guide to a PhD
https://karpathy.github.io/2016/09/07/phd/ https://t.iss.one/ArtificialIntelligenceArticles

Cross-lingual transfer is a powerful tool for low-resource NLP. But when you build a system for a new language (say Bengali, German or French), what language do you transfer from?

This paper answers this: https://www.profillic.com/paper/arxiv:1905.12688

PyFastBertTorch

https://medium.com/huggingface/introducing-fastbert-a-simple-deep-learning-library-for-bert-models-89ff763ad384

COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration
Watters et al.: https://arxiv.org/abs/1905.09275
#MachineLearning #UnsupervisedLearning #ArtificialIntelligence

A very nice article on practical limitations of semi-supervised learning and the recent advances that seems to overcome them
[https://towardsdatascience.com/the-quiet-semi-supervised-revolution-edec1e9ad8c]


(https://towardsdatascience.com/the-quiet-semi-supervised-revolution-edec1e9ad8c)


#machinelearning #artificialintelligence