107,745 research outputs found
Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and Regression
Knowledge distillation (KD) has been actively studied for image
classification tasks in deep learning, aiming to improve the performance of a
student model based on the knowledge from a teacher model. However, there have
been very few efforts for applying KD in image regression with a scalar
response, and there is no KD method applicable to both tasks. Moreover,
existing KD methods often require a practitioner to carefully choose or adjust
the teacher and student architectures, making these methods less scalable in
practice. Furthermore, although KD is usually conducted in scenarios with
limited labeled data, very few techniques are developed to alleviate such data
insufficiency. To solve the above problems in an all-in-one manner, we propose
in this paper a unified KD framework based on conditional generative
adversarial networks (cGANs), termed cGAN-KD. Fundamentally different from
existing KD methods, cGAN-KD distills and transfers knowledge from a teacher
model to a student model via cGAN-generated samples. This unique mechanism
makes cGAN-KD suitable for both classification and regression tasks, compatible
with other KD methods, and insensitive to the teacher and student
architectures. Also, benefiting from the recent advances in cGAN methodology
and our specially designed subsampling and filtering procedures, cGAN-KD also
performs well when labeled data are scarce. An error bound of a student model
trained in the cGAN-KD framework is derived in this work, which theoretically
explains why cGAN-KD takes effect and guides the implementation of cGAN-KD in
practice. Extensive experiments on CIFAR-10 and Tiny-ImageNet show that we can
incorporate state-of-the-art KD methods into the cGAN-KD framework to reach a
new state of the art. Also, experiments on RC-49 and UTKFace demonstrate the
effectiveness of cGAN-KD in image regression tasks, where existing KD methods
are inapplicable
ECG Arrhythmia Classification Using Transfer Learning from 2-Dimensional Deep CNN Features
Due to the recent advances in the area of deep learning, it has been
demonstrated that a deep neural network, trained on a huge amount of data, can
recognize cardiac arrhythmias better than cardiologists. Moreover,
traditionally feature extraction was considered an integral part of ECG pattern
recognition; however, recent findings have shown that deep neural networks can
carry out the task of feature extraction directly from the data itself. In
order to use deep neural networks for their accuracy and feature extraction,
high volume of training data is required, which in the case of independent
studies is not pragmatic. To arise to this challenge, in this work, the
identification and classification of four ECG patterns are studied from a
transfer learning perspective, transferring knowledge learned from the image
classification domain to the ECG signal classification domain. It is
demonstrated that feature maps learned in a deep neural network trained on
great amounts of generic input images can be used as general descriptors for
the ECG signal spectrograms and result in features that enable classification
of arrhythmias. Overall, an accuracy of 97.23 percent is achieved in
classifying near 7000 instances by ten-fold cross validation.Comment: Accepted and presented for IEEE Biomedical Circuits and Systems
(BioCAS) on 17th-19th October 2018 in Ohio, US
Knowledge Transfer via Multi-Head Feature Adaptation for Whole Slide Image Classification
Transferring prior knowledge from a source domain to the same or similar
target domain can greatly enhance the performance of models on the target
domain. However, it is challenging to directly leverage the knowledge from the
source domain due to task discrepancy and domain shift. To bridge the gaps
between different tasks and domains, we propose a Multi-Head Feature Adaptation
module, which projects features in the source feature space to a new space that
is more similar to the target space. Knowledge transfer is particularly
important in Whole Slide Image (WSI) classification since the number of WSIs in
one dataset might be too small to achieve satisfactory performance. Therefore,
WSI classification is an ideal testbed for our method, and we adapt multiple
knowledge transfer methods for WSI classification. The experimental results
show that models with knowledge transfer outperform models that are trained
from scratch by a large margin regardless of the number of WSIs in the
datasets, and our method achieves state-of-the-art performances among other
knowledge transfer methods on multiple datasets, including TCGA-RCC,
TCGA-NSCLC, and Camelyon16 datasets
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