687 research outputs found
Invariant Representations through Adversarial Forgetting
We propose a novel approach to achieving invariance for deep neural networks
in the form of inducing amnesia to unwanted factors of data through a new
adversarial forgetting mechanism. We show that the forgetting mechanism serves
as an information-bottleneck, which is manipulated by the adversarial training
to learn invariance to unwanted factors. Empirical results show that the
proposed framework achieves state-of-the-art performance at learning invariance
in both nuisance and bias settings on a diverse collection of datasets and
tasks.Comment: To appear in Proceedings of the 34th AAAI Conference on Artificial
Intelligence (AAAI-20
Incremental Adversarial Domain Adaptation for Continually Changing Environments
Continuous appearance shifts such as changes in weather and lighting
conditions can impact the performance of deployed machine learning models.
While unsupervised domain adaptation aims to address this challenge, current
approaches do not utilise the continuity of the occurring shifts. In
particular, many robotics applications exhibit these conditions and thus
facilitate the potential to incrementally adapt a learnt model over minor
shifts which integrate to massive differences over time. Our work presents an
adversarial approach for lifelong, incremental domain adaptation which benefits
from unsupervised alignment to a series of intermediate domains which
successively diverge from the labelled source domain. We empirically
demonstrate that our incremental approach improves handling of large appearance
changes, e.g. day to night, on a traversable-path segmentation task compared
with a direct, single alignment step approach. Furthermore, by approximating
the feature distribution for the source domain with a generative adversarial
network, the deployment module can be rendered fully independent of retaining
potentially large amounts of the related source training data for only a minor
reduction in performance.Comment: International Conference on Robotics and Automation 201
Omnidirectional Transfer for Quasilinear Lifelong Learning
In biological learning, data are used to improve performance not only on the
current task, but also on previously encountered and as yet unencountered
tasks. In contrast, classical machine learning starts from a blank slate, or
tabula rasa, using data only for the single task at hand. While typical
transfer learning algorithms can improve performance on future tasks, their
performance on prior tasks degrades upon learning new tasks (called
catastrophic forgetting). Many recent approaches for continual or lifelong
learning have attempted to maintain performance given new tasks. But striving
to avoid forgetting sets the goal unnecessarily low: the goal of lifelong
learning, whether biological or artificial, should be to improve performance on
all tasks (including past and future) with any new data. We propose
omnidirectional transfer learning algorithms, which includes two special cases
of interest: decision forests and deep networks. Our key insight is the
development of the omni-voter layer, which ensembles representations learned
independently on all tasks to jointly decide how to proceed on any given new
data point, thereby improving performance on both past and future tasks. Our
algorithms demonstrate omnidirectional transfer in a variety of simulated and
real data scenarios, including tabular data, image data, spoken data, and
adversarial tasks. Moreover, they do so with quasilinear space and time
complexity
Attentive Single-Tasking of Multiple Tasks
In this work we address task interference in universal networks by
considering that a network is trained on multiple tasks, but performs one task
at a time, an approach we refer to as "single-tasking multiple tasks". The
network thus modifies its behaviour through task-dependent feature adaptation,
or task attention. This gives the network the ability to accentuate the
features that are adapted to a task, while shunning irrelevant ones. We further
reduce task interference by forcing the task gradients to be statistically
indistinguishable through adversarial training, ensuring that the common
backbone architecture serving all tasks is not dominated by any of the
task-specific gradients. Results in three multi-task dense labelling problems
consistently show: (i) a large reduction in the number of parameters while
preserving, or even improving performance and (ii) a smooth trade-off between
computation and multi-task accuracy. We provide our system's code and
pre-trained models at http://vision.ee.ethz.ch/~kmaninis/astmt/.Comment: CVPR 2019 Camera Read
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