3 research outputs found
Semi-Supervised Learning via New Deep Network Inversion
We exploit a recently derived inversion scheme for arbitrary deep neural
networks to develop a new semi-supervised learning framework that applies to a
wide range of systems and problems. The approach outperforms current
state-of-the-art methods on MNIST reaching of test set accuracy while
using labeled examples per class. Experiments with one-dimensional signals
highlight the generality of the method. Importantly, our approach is simple,
efficient, and requires no change in the deep network architecture.Comment: arXiv admin note: substantial text overlap with arXiv:1710.0930
Workshop Report: Detection and Classification in Marine Bioacoustics with Deep Learning
On 21-22 November 2019, about 30 researchers gathered in Victoria, BC,
Canada, for the workshop "Detection and Classification in Marine Bioacoustics
with Deep Learning" organized by MERIDIAN and hosted by Ocean Networks Canada.
The workshop was attended by marine biologists, data scientists, and computer
scientists coming from both Canadian coasts and the US and representing a wide
spectrum of research organizations including universities, government
(Fisheries and Oceans Canada, National Oceanic and Atmospheric Administration),
industry (JASCO Applied Sciences, Google, Axiom Data Science), and
non-for-profits (Orcasound, OrcaLab). Consisting of a mix of oral
presentations, open discussion sessions, and hands-on tutorials, the workshop
program offered a rare opportunity for specialists from distinctly different
domains to engage in conversation about deep learning and its promising
potential for the development of detection and classification algorithms in
underwater acoustics. In this workshop report, we summarize key points from the
presentations and discussion sessions.Comment: 13 pages, 1 figure, 1 tabl
Adversarial Code Learning for Image Generation
We introduce the "adversarial code learning" (ACL) module that improves
overall image generation performance to several types of deep models. Instead
of performing a posterior distribution modeling in the pixel spaces of
generators, ACLs aim to jointly learn a latent code with another image
encoder/inference net, with a prior noise as its input. We conduct the learning
in an adversarial learning process, which bears a close resemblance to the
original GAN but again shifts the learning from image spaces to prior and
latent code spaces. ACL is a portable module that brings up much more
flexibility and possibilities in generative model designs. First, it allows
flexibility to convert non-generative models like Autoencoders and standard
classification models to decent generative models. Second, it enhances existing
GANs' performance by generating meaningful codes and images from any part of
the prior. We have incorporated our ACL module with the aforementioned
frameworks and have performed experiments on synthetic, MNIST, CIFAR-10, and
CelebA datasets. Our models have achieved significant improvements which
demonstrated the generality for image generation tasks