23,090 research outputs found
Seeing What a GAN Cannot Generate
Despite the success of Generative Adversarial Networks (GANs), mode collapse
remains a serious issue during GAN training. To date, little work has focused
on understanding and quantifying which modes have been dropped by a model. In
this work, we visualize mode collapse at both the distribution level and the
instance level. First, we deploy a semantic segmentation network to compare the
distribution of segmented objects in the generated images with the target
distribution in the training set. Differences in statistics reveal object
classes that are omitted by a GAN. Second, given the identified omitted object
classes, we visualize the GAN's omissions directly. In particular, we compare
specific differences between individual photos and their approximate inversions
by a GAN. To this end, we relax the problem of inversion and solve the
tractable problem of inverting a GAN layer instead of the entire generator.
Finally, we use this framework to analyze several recent GANs trained on
multiple datasets and identify their typical failure cases.Comment: ICCV 2019 oral; http://ganseeing.csail.mit.edu
Interpretations or Interventions? Indian philosophy in the global cosmopolis
This introduction concerns the place that Indian philosophical literature should occupy in the history of philosophy, and the challenge of championing pre-modern modes of inquiry in an era when philosophy, at least in the anglophone world and its satellites, has in large measure become a highly specialized and technical discipline conceived on the model of the sciences. This challenge is particularly acute when philosophical figures and texts that are historically and culturally distant from us are engaged not only exegetically but also with a view to recruiting their topics and arguments for contemporary philosophical debates
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Deep Learning has recently become hugely popular in machine learning,
providing significant improvements in classification accuracy in the presence
of highly-structured and large databases.
Researchers have also considered privacy implications of deep learning.
Models are typically trained in a centralized manner with all the data being
processed by the same training algorithm. If the data is a collection of users'
private data, including habits, personal pictures, geographical positions,
interests, and more, the centralized server will have access to sensitive
information that could potentially be mishandled. To tackle this problem,
collaborative deep learning models have recently been proposed where parties
locally train their deep learning structures and only share a subset of the
parameters in the attempt to keep their respective training sets private.
Parameters can also be obfuscated via differential privacy (DP) to make
information extraction even more challenging, as proposed by Shokri and
Shmatikov at CCS'15.
Unfortunately, we show that any privacy-preserving collaborative deep
learning is susceptible to a powerful attack that we devise in this paper. In
particular, we show that a distributed, federated, or decentralized deep
learning approach is fundamentally broken and does not protect the training
sets of honest participants. The attack we developed exploits the real-time
nature of the learning process that allows the adversary to train a Generative
Adversarial Network (GAN) that generates prototypical samples of the targeted
training set that was meant to be private (the samples generated by the GAN are
intended to come from the same distribution as the training data).
Interestingly, we show that record-level DP applied to the shared parameters of
the model, as suggested in previous work, is ineffective (i.e., record-level DP
is not designed to address our attack).Comment: ACM CCS'17, 16 pages, 18 figure
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