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ML Digest: Explainable AI & Scaling Training
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Welcome to this week of the Best of Machine Learning Digest. In this weekly newsletter, we
resurface some of the best resources in Machine Learning posted in the past week. This time,
we've gotten 57 submissions, including 3 papers.
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Thanks to our collaborators for their help. We'd like more people on the team! Submit a resource.
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Papers
This week some new
Papers were posted on Best of ML. In
the
following, we're showing you the Top 3 posts of this week.
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PointRend: Image Segmentation as Rendering
We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. From this vantage, we present the PointRend (Point-based Rendering) neural network module: a module that performs point-based segmentation predictions at adaptively selected locations based on an iterative subdivision algorithm. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. While many concrete implementations of the general idea are possible, we show that a simple design already achieves excellent results. Qualitatively, PointRend outputs crisp object boundaries in regions that are over-smoothed by previous methods. Quantitatively, PointRend yields significant gains on COCO and Cityscapes, for both instance and semantic segmentation. PointRend's efficiency enables output resolutions that are otherwise impractical in terms of memory or computation compared to existing approaches.
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secml: A Python Library for Secure and Explainable Machine Learning
We present secml, an open-source Python library for secure and explainable machine learning. It implements the most popular attacks against machine learning, including not only test-time evasion attacks to generate adversarial examples against deep neural networks, but also training-time poisoning attacks against support vector machines and many other algorithms. These attacks enable evaluating the security of learning algorithms and of the corresponding defenses under both white-box and black-box threat models. To this end, secml provides built-in functions to compute security evaluation curves, showing how quickly classification performance decreases against increasing adversarial perturbations of the input data. secml also includes explainability methods to help understand why adversarial attacks succeed against a given model, by visualizing the most influential features and training prototypes contributing to each decision.
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Characterizing the Decision Boundary of Deep Neural Networks
Deep neural networks and in particular, deep neural classifiers have become an integral part of many modern applications. Despite their practical success, we still have limited knowledge of how they work and the demand for such an understanding is evergrowing. In this regard, one crucial aspect of deep neural network classifiers that can help us deepen our knowledge about their decision-making behavior is to investigate their decision boundaries. Nevertheless, this is contingent upon having access to samples populating the areas near the decision boundary. To achieve this, we propose a novel approach we call Deep Decision boundary Instance Generation (DeepDIG).
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Blog Posts
This week, 35
Blog Posts were posted on Best of ML. In
the
following, we're showing you the Top 2 posts of this week.
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How to scale training on multiple GPUs
One of the biggest problems with Deep Learning models is that they are becoming too big to train in a single GPU. If the current models were trained in a single GPU, they would take too long. In order to train models in a timely fashion, it is necessary to train them with multiple GPUs.
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Multi-View Image Classification
Not long ago, I took part in a machine learning hackathon hosted by Daimler-Benz. The problem we were presented with was rather interesting and not so common. So I decided to write an article about it, in case my approach(es) can help someone else faced with a similar task.
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