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Poster
Tagger: Deep Unsupervised Perceptual Grouping
Klaus Greff · Antti Rasmus · Mathias Berglund · Hotloo Xiranood · Harri Valpola · Jürgen Schmidhuber

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #60 #None

We present a framework for efficient perceptual inference that explicitly reasons about the segmentation of its inputs and features. Rather than being trained for any specific segmentation, our framework learns the grouping process in an unsupervised manner or alongside any supervised task. We enable a neural network to group the representations of different objects in an iterative manner through a differentiable mechanism. We achieve very fast convergence by allowing the system to amortize the joint iterative inference of the groupings and their representations. In contrast to many other recently proposed methods for addressing multi-object scenes, our system does not assume the inputs to be images and can therefore directly handle other modalities. We evaluate our method on multi-digit classification of very cluttered images that require texture segmentation. Remarkably our method achieves improved classification performance over convolutional networks despite being fully connected, by making use of the grouping mechanism. Furthermore, we observe that our system greatly improves upon the semi-supervised result of a baseline Ladder network on our dataset. These results are evidence that grouping is a powerful tool that can help to improve sample efficiency.

Author Information

Klaus Greff (IDSIA)
Antti Rasmus (The Curious AI Company)
Mathias Berglund (The Curious AI Company)
Hotloo Xiranood Xiranood (The Curious AI Company)
Harri Valpola (The Curious AI Company)
Jürgen Schmidhuber (Swiss AI Lab, IDSIA (USI & SUPSI) - NNAISENSE)

Since age 15, his main goal has been to build an Artificial Intelligence smarter than himself, then retire. The Deep Learning Artificial Neural Networks developed since 1991 by his research groups have revolutionised handwriting recognition, speech recognition, machine translation, image captioning, and are now available to billions of users through Google, Microsoft, IBM, Baidu, and many other companies (DeepMind also was heavily influenced by his lab). His team's Deep Learners were the first to win object detection and image segmentation contests, and achieved the world's first superhuman visual classification results, winning nine international competitions in machine learning & pattern recognition. His formal theory of fun & creativity & curiosity explains art, science, music, and humor. He has published 333 papers, earned 7 best paper/best video awards, the 2013 Helmholtz Award of the International Neural Networks Society, and the 2016 IEEE Neural Networks Pioneer Award. He is also president of NNAISENSE, which aims at building the first practical general purpose AI.

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