5,658 research outputs found
Unsupervised Deep Learning by Neighbourhood Discovery
Deep convolutional neural networks (CNNs) have demonstrated remarkable
success in computer vision by supervisedly learning strong visual feature
representations. However, training CNNs relies heavily on the availability of
exhaustive training data annotations, limiting significantly their deployment
and scalability in many application scenarios. In this work, we introduce a
generic unsupervised deep learning approach to training deep models without the
need for any manual label supervision. Specifically, we progressively discover
sample anchored/centred neighbourhoods to reason and learn the underlying class
decision boundaries iteratively and accumulatively. Every single neighbourhood
is specially formulated so that all the member samples can share the same
unseen class labels at high probability for facilitating the extraction of
class discriminative feature representations during training. Experiments on
image classification show the performance advantages of the proposed method
over the state-of-the-art unsupervised learning models on six benchmarks
including both coarse-grained and fine-grained object image categorisation.Comment: 36th International Conference on Machine Learning (ICML'19). Code is
available at https://github.com/Raymond-sci/AN
Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography
Automated segmentation of retinal blood vessels in label-free fundus images
entails a pivotal role in computed aided diagnosis of ophthalmic pathologies,
viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases.
The challenge remains active in medical image analysis research due to varied
distribution of blood vessels, which manifest variations in their dimensions of
physical appearance against a noisy background.
In this paper we formulate the segmentation challenge as a classification
task. Specifically, we employ unsupervised hierarchical feature learning using
ensemble of two level of sparsely trained denoised stacked autoencoder. First
level training with bootstrap samples ensures decoupling and second level
ensemble formed by different network architectures ensures architectural
revision. We show that ensemble training of auto-encoders fosters diversity in
learning dictionary of visual kernels for vessel segmentation. SoftMax
classifier is used for fine tuning each member auto-encoder and multiple
strategies are explored for 2-level fusion of ensemble members. On DRIVE
dataset, we achieve maximum average accuracy of 95.33\% with an impressively
low standard deviation of 0.003 and Kappa agreement coefficient of 0.708 .
Comparison with other major algorithms substantiates the high efficacy of our
model.Comment: Accepted as a conference paper at IEEE EMBC, 201
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