29,024 research outputs found
Secost: Sequential co-supervision for large scale weakly labeled audio event detection
Weakly supervised learning algorithms are critical for scaling audio event
detection to several hundreds of sound categories. Such learning models should
not only disambiguate sound events efficiently with minimal class-specific
annotation but also be robust to label noise, which is more apparent with weak
labels instead of strong annotations. In this work, we propose a new framework
for designing learning models with weak supervision by bridging ideas from
sequential learning and knowledge distillation. We refer to the proposed
methodology as SeCoST (pronounced Sequest) -- Sequential Co-supervision for
training generations of Students. SeCoST incrementally builds a cascade of
student-teacher pairs via a novel knowledge transfer method. Our evaluations on
Audioset (the largest weakly labeled dataset available) show that SeCoST
achieves a mean average precision of 0.383 while outperforming prior state of
the art by a considerable margin.Comment: Accepted IEEE ICASSP 202
Enthusing and inspiring with reusable kinaesthetic activities
We describe the experiences of three University projects that use a style of physical, non-computer based activity to enthuse and teach school students computer science concepts. We show that this kind of activity is effective as an outreach and teaching resource even when reused across different age/ability ranges, in lecture and workshop formats and for delivery by different people. We introduce the concept of a Reusable Outreach Object (ROO) that extends Reusable Learning Objects. and argue for a community effort in developing a repository of such objects
Using Scratch to Teach Undergraduate Students' Skills on Artificial Intelligence
This paper presents a educational workshop in Scratch that is proposed for
the active participation of undergraduate students in contexts of Artificial
Intelligence. The main objective of the activity is to demystify the complexity
of Artificial Intelligence and its algorithms. For this purpose, students must
realize simple exercises of clustering and two neural networks, in Scratch. The
detailed methodology to get that is presented in the article.Comment: 6 pages, 7 figures, workshop presentatio
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation
Convolutional neural networks have been widely deployed in various
application scenarios. In order to extend the applications' boundaries to some
accuracy-crucial domains, researchers have been investigating approaches to
boost accuracy through either deeper or wider network structures, which brings
with them the exponential increment of the computational and storage cost,
delaying the responding time. In this paper, we propose a general training
framework named self distillation, which notably enhances the performance
(accuracy) of convolutional neural networks through shrinking the size of the
network rather than aggrandizing it. Different from traditional knowledge
distillation - a knowledge transformation methodology among networks, which
forces student neural networks to approximate the softmax layer outputs of
pre-trained teacher neural networks, the proposed self distillation framework
distills knowledge within network itself. The networks are firstly divided into
several sections. Then the knowledge in the deeper portion of the networks is
squeezed into the shallow ones. Experiments further prove the generalization of
the proposed self distillation framework: enhancement of accuracy at average
level is 2.65%, varying from 0.61% in ResNeXt as minimum to 4.07% in VGG19 as
maximum. In addition, it can also provide flexibility of depth-wise scalable
inference on resource-limited edge devices.Our codes will be released on github
soon.Comment: 10page
Fidelity-Weighted Learning
Training deep neural networks requires many training samples, but in practice
training labels are expensive to obtain and may be of varying quality, as some
may be from trusted expert labelers while others might be from heuristics or
other sources of weak supervision such as crowd-sourcing. This creates a
fundamental quality versus-quantity trade-off in the learning process. Do we
learn from the small amount of high-quality data or the potentially large
amount of weakly-labeled data? We argue that if the learner could somehow know
and take the label-quality into account when learning the data representation,
we could get the best of both worlds. To this end, we propose
"fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach
for training deep neural networks using weakly-labeled data. FWL modulates the
parameter updates to a student network (trained on the task we care about) on a
per-sample basis according to the posterior confidence of its label-quality
estimated by a teacher (who has access to the high-quality labels). Both
student and teacher are learned from the data. We evaluate FWL on two tasks in
information retrieval and natural language processing where we outperform
state-of-the-art alternative semi-supervised methods, indicating that our
approach makes better use of strong and weak labels, and leads to better
task-dependent data representations.Comment: Published as a conference paper at ICLR 201
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