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Author: torontoai

[R] Adversarial explanations for understanding image classification decisions and improved neural network robustness

Written on . Posted in Reddit MachineLearning.

[R] Adversarial explanations for understanding image classification decisions and improved neural network robustness

Abstract:

For sensitive problems, such as medical imaging or fraud detection, neural network (NN) adoption has been slow due to concerns about their reliability, leading to a number of algorithms for explaining their decisions. NNs have also been found to be vulnerable to a class of imperceptible attacks, called adversarial examples, which arbitrarily alter the output of the network. Here we demonstrate both that these attacks can invalidate previous attempts to explain the decisions of NNs, and that with very robust networks, the attacks themselves may be leveraged as explanations with greater fidelity to the model. We also show that the introduction of a novel regularization technique inspired by the Lipschitz constraint, alongside other proposed improvements including a half-Huber activation function, greatly improves the resistance of NNs to adversarial examples. On the ImageNet classification task, we demonstrate a network with an accuracy-robustness area (ARA) of 0.0053, an ARA 2.4 times greater than the previous state-of-the-art value. Improving the mechanisms by which NN decisions are understood is an important direction for both establishing trust in sensitive domains and learning more about the stimuli to which NNs respond.

Open Access pre-print: https://arxiv.org/abs/1906.02896

Open Access PDF (low-resolution images, due to size restriction): https://arxiv.org/pdf/1906.02896.pdf

Peer-reviewed publication (with full-resolution images; also see bottom of this Reddit post): https://www.nature.com/articles/s42256-019-0104-6

Code: https://github.com/wwoods/adversarial-explanations-cifar/

Comparing explanatory power between Grad-CAM [Selvaraju et al. 2017] and Adversarial Explanations (AEs) when applied to a robust NN trained on CIFAR-10. The top four rows, subfigure a, demonstrate comparisons on different inputs. For each row, the columns show: the original “Input” image, labeled with the most confidently-predicted class, the correct class, and the NN’s confidence in each; two Grad-CAM explanations, one for each predicted class shown by the input; two AEs, divided into the adversarial noise used to produce the AE, and the AE itself. Below those rows, subfigures b through i are annotated versions of the AEs for subfigure a, indicating regions which contributed to or detracted from each predicted class. See the main text for full commentary.

Author’s note: The freely-available pre-print on ArXiv contains all content available in the Nature version, just in a slightly different ordering (IEEE vs Nature style). The resolution of the ArXiv images is a bit lower, as the full document from pdflatex is ~97 MB due to included images… A Ghostscript-optimized version, with full-resolution images, weighs in at 25MB and may be found here: https://drive.google.com/open?id=1xGCja0BUQ2VR9nlKre6QzJ2Q-qpp8ub8

submitted by /u/waltywalt
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[R] Learning similarity measures from data: Extended Siamese Neural Networks

Written on . Posted in Reddit MachineLearning.

Short summary: We extend the state of the art of using neural networks to learn similarity measures (learning distance between pairs of data points in a dataset) with a new method called Extended Siamese Neural Networks (eSNN). This new method is put into the context of a framework that is used to describe different types of similarity measures. eSNN outperforms all current methods, while at the same time requiring the least training of all methods (down to half of the training of some comparable methods).
Paper link (Open Access): https://link.springer.com/article/10.1007/s13748-019-00201-2
PDF (Open Access): https://link.springer.com/content/pdf/10.1007%2Fs13748-019-00201-2.pdf
Code: https://github.com/ntnu-ai-lab/eSNN/

Authors: Bjørn Magnus Mathisen, Agnar Aamodt, Kerstin Bach and Helge Langseth

Abstract:

Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or a set of cases most similar to the query case. Describing a similarity measure analytically is challenging, even for domain experts working with CBR experts. However, datasets are typically gathered as part of constructing a CBR or machine learning system. These datasets are assumed to contain the features that correctly identify the solution from the problem features; thus, they may also contain the knowledge to construct or learn such a similarity measure. The main motivation for this work is to automate the construction of similarity measures using machine learning. Additionally, we would like to do this while keeping training time as low as possible. Working toward this, our objective is to investigate how to apply machine learning to effectively learn a similarity measure. Such a learned similarity measure could be used for CBR systems, but also for clustering data in semi-supervised learning, or one-shot learning tasks. Recent work has advanced toward this goal which relies on either very long training times or manually modeling parts of the similarity measure. We created a framework to help us analyze the current methods for learning similarity measures. This analysis resulted in two novel similarity measure designs: The first design uses a pre-trained classifier as basis for a similarity measure, and the second design uses as little modeling as possible while learning the similarity measure from data and keeping training time low. Both similarity measures were evaluated on 14 different datasets. The evaluation shows that using a classifier as basis for a similarity measure gives state-of-the-art performance. Finally, the evaluation shows that our fully data-driven similarity measure design outperforms state-of-the-art methods while keeping training time low.

Hope you like my work. I will try to answer questions if you have any.

submitted by /u/epic
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[D] 2020 Residencies Applicants Discussion Thread

Written on . Posted in Reddit MachineLearning.

  • Facebook AI Residency Program [Link]. Application Deadline: January 31, 2020, 05:00pm PST.
  • Google AI Residency [Link]. Application Deadline: December 19th, 2019.
  • Google X AI Residency [Link]
  • Google AI Resident (Health), 2020 Start – London, UK [Application Closed]
  • Google AI Resident (Health), 2020 – Start Palo Alto, CA, USA [Application Closed]
  • OpenAI 2020 Winter Scholars [Link]. Application Deadline: Nov 15, 2019.

Thought it would be helpful to have a discussion thread for 2020 Residencies applicants to share the updates, info, resources to prepare etc.

Below are some useful discussion threads :

https://www.reddit.com/r/MachineLearning/comments/9uyzc1/d_google_ai_residency_2019_applicants_discussion/

https://www.reddit.com/r/MachineLearning/comments/7rajic/d_anyone_heard_back_from_google_ai_residency/

https://www.reddit.com/r/MachineLearning/comments/7wst07/d_study_guides_for_interview_at_ai_research/

https://www.reddit.com/r/MachineLearning/comments/690ixs/d_google_brain_residency_requirements_and/

submitted by /u/mahaveer0suthar
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[D] The Machine Learning Conference is next week – Free ticket code here

Written on . Posted in Reddit MachineLearning.

Hi All, as a thank you to this community, we’re giving away 5 free tickets to MLconf SF. The event is almost sold out so first to register gets the tickets: https://www.eventbrite.com/e/mlconf-sf-2019-tickets-52641374769

For free registration use code: slashmlfree

Note: The code will expire once the 5 free tickets are registered, they’re gone. First come first served. If the code stops working you may still be able to use to 50% off code: slashml19

*Please don’t share the code, we’re like these tickets to go to community members.

**If you’ve already purchased a ticket, you’re not eligible for a refund.

For video from past events see: https://www.youtube.com/channel/UCjeM1xxYb_37bZfyparLS3Q/videos

submitted by /u/shonburton
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