23rd April - 2nd May 2019

Etymo Newsletter provides the latest development in machine learning research, including the most popular datasets and the most trending papers in the past two weeks.

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Fortnight Summary

Popular datasets include MNIST, ImageNet, COCO, and KITTI. Our trending phrases are Light Field, Truth Discovery, and Pairwise Learning. Three trending papers are related to GCNet, Unsupervised Data Augementation, and an image extractor using local retention networks. We also present you a collection of recent review papers in AI/Machine Learning.

Popular Datasets

Here are most mentioned datasets over the last two weeks.

Name Type Number of Papers
MNIST Handwritten Digits 58
ImageNet Image Dataset 35
CIFAR-10 Tiny Image Dataset in 10 Classes 30
COCO Common Objects in Context 21
KITTI Autonomous Driving 14
Cityscapes Images from 50 different cities 11
CIFAR-100 Tiny Image Dataset in 100 Classes 11

Trending Phrases

In this section, we present a list of phrases that appeared significantly more in this newsletter than the previous newsletters.

Etymo Trending

Presented below is a list of the most trending papers added in the last two weeks.

  • GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond:
    The authors designed a global context network (GCNet), which unifies both simplified NLNet and SENet and enhances it with a lightweight instantiation. GCNet generally outperforms both simplified NLNet and SENet on major benchmarks for various recognition tasks. The code and configurations are also available.

  • Unsupervised Data Augmentation:
    This paper proposes to apply data augmentation to unlabeled data in a semi-supervised learning setting. The method, named Unsupervised Data Augmentation or UDA, encourages the model predictions to be consistent between an unlabeled example and an augmented unlabeled example, by using state-of-the-art data augmentation methods.

  • Local Relation Networks for Image Recognition:
    The authors present a new image feature extractor, called the local relation layer, that adaptively determines aggregation weights based on the compositional relationship of local pixel pairs. It can composite visual elements into higher-level entities in a more efficient manner that benefits semantic inference.

A Collection of Notable Review Papers

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