2 research outputs found
ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference
Whereas the ability of deep networks to produce useful predictions on many
kinds of data has been amply demonstrated, estimating the reliability of these
predictions remains challenging. Sampling approaches such as MC-Dropout and
Deep Ensembles have emerged as the most popular ones for this purpose.
Unfortunately, they require many forward passes at inference time, which slows
them down. Sampling-free approaches can be faster but suffer from other
drawbacks, such as lower reliability of uncertainty estimates, difficulty of
use, and limited applicability to different types of tasks and data.
In this work, we introduce a sampling-free approach that is generic and easy
to deploy, while producing reliable uncertainty estimates on par with
state-of-the-art methods at a significantly lower computational cost. It is
predicated on training the network to produce the same output with and without
additional information about that output. At inference time, when no prior
information is given, we use the network's own prediction as the additional
information. We prove that the difference between the two predictions is an
accurate uncertainty estimate and demonstrate our approach on various types of
tasks and applications
PartAL: Efficient Partial Active Learning in Multi-Task Visual Settings
Multi-task learning is central to many real-world applications.
Unfortunately, obtaining labelled data for all tasks is time-consuming,
challenging, and expensive. Active Learning (AL) can be used to reduce this
burden. Existing techniques typically involve picking images to be annotated
and providing annotations for all tasks.
In this paper, we show that it is more effective to select not only the
images to be annotated but also a subset of tasks for which to provide
annotations at each AL iteration. Furthermore, the annotations that are
provided can be used to guess pseudo-labels for the tasks that remain
unannotated. We demonstrate the effectiveness of our approach on several
popular multi-task datasets