9,914 research outputs found
Learning Task Relatedness in Multi-Task Learning for Images in Context
Multimedia applications often require concurrent solutions to multiple tasks.
These tasks hold clues to each-others solutions, however as these relations can
be complex this remains a rarely utilized property. When task relations are
explicitly defined based on domain knowledge multi-task learning (MTL) offers
such concurrent solutions, while exploiting relatedness between multiple tasks
performed over the same dataset. In most cases however, this relatedness is not
explicitly defined and the domain expert knowledge that defines it is not
available. To address this issue, we introduce Selective Sharing, a method that
learns the inter-task relatedness from secondary latent features while the
model trains. Using this insight, we can automatically group tasks and allow
them to share knowledge in a mutually beneficial way. We support our method
with experiments on 5 datasets in classification, regression, and ranking tasks
and compare to strong baselines and state-of-the-art approaches showing a
consistent improvement in terms of accuracy and parameter counts. In addition,
we perform an activation region analysis showing how Selective Sharing affects
the learned representation.Comment: To appear in ICMR 2019 (Oral + Lightning Talk + Poster
Dissecting Deep Learning Networks—Visualizing Mutual Information
Deep Learning (DL) networks are recent revolutionary developments in artificial intelligence research. Typical networks are stacked by groups of layers that are further composed of many convolutional kernels or neurons. In network design, many hyper-parameters need to be defined heuristically before training in order to achieve high cross-validation accuracies. However, accuracy evaluation from the output layer alone is not sufficient to specify the roles of the hidden units in associated networks. This results in a significant knowledge gap between DL’s wider applications and its limited theoretical understanding. To narrow the knowledge gap, our study explores visualization techniques to illustrate the mutual information (MI) in DL networks. The MI is a theoretical measurement, reflecting the relationship between two sets of random variables even if their relationship is highly non-linear and hidden in high-dimensional data. Our study aims to understand the roles of DL units in classification performance of the networks. Via a series of experiments using several popular DL networks, it shows that the visualization of MI and its change patterns between the input/output with the hidden layers and basic units can facilitate a better understanding of these DL units’ roles. Our investigation on network convergence suggests a more objective manner to potentially evaluate DL networks. Furthermore, the visualization provides a useful tool to gain insights into the network performance, and thus to potentially facilitate the design of better network architectures by identifying redundancy and less-effective network units
Privacy-Preserving Adversarial Networks
We propose a data-driven framework for optimizing privacy-preserving data
release mechanisms to attain the information-theoretically optimal tradeoff
between minimizing distortion of useful data and concealing specific sensitive
information. Our approach employs adversarially-trained neural networks to
implement randomized mechanisms and to perform a variational approximation of
mutual information privacy. We validate our Privacy-Preserving Adversarial
Networks (PPAN) framework via proof-of-concept experiments on discrete and
continuous synthetic data, as well as the MNIST handwritten digits dataset. For
synthetic data, our model-agnostic PPAN approach achieves tradeoff points very
close to the optimal tradeoffs that are analytically-derived from model
knowledge. In experiments with the MNIST data, we visually demonstrate a
learned tradeoff between minimizing the pixel-level distortion versus
concealing the written digit.Comment: 16 page
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