3,399 research outputs found
Hierarchically Clustered Representation Learning
The joint optimization of representation learning and clustering in the
embedding space has experienced a breakthrough in recent years. In spite of the
advance, clustering with representation learning has been limited to flat-level
categories, which often involves cohesive clustering with a focus on instance
relations. To overcome the limitations of flat clustering, we introduce
hierarchically-clustered representation learning (HCRL), which simultaneously
optimizes representation learning and hierarchical clustering in the embedding
space. Compared with a few prior works, HCRL firstly attempts to consider a
generation of deep embeddings from every component of the hierarchy, not just
leaf components. In addition to obtaining hierarchically clustered embeddings,
we can reconstruct data by the various abstraction levels, infer the intrinsic
hierarchical structure, and learn the level-proportion features. We conducted
evaluations with image and text domains, and our quantitative analyses showed
competent likelihoods and the best accuracies compared with the baselines.Comment: 10 pages, 7 figures, Under review as a conference pape
Adversarial Dropout for Supervised and Semi-supervised Learning
Recently, the training with adversarial examples, which are generated by
adding a small but worst-case perturbation on input examples, has been proved
to improve generalization performance of neural networks. In contrast to the
individually biased inputs to enhance the generality, this paper introduces
adversarial dropout, which is a minimal set of dropouts that maximize the
divergence between the outputs from the network with the dropouts and the
training supervisions. The identified adversarial dropout are used to
reconfigure the neural network to train, and we demonstrated that training on
the reconfigured sub-network improves the generalization performance of
supervised and semi-supervised learning tasks on MNIST and CIFAR-10. We
analyzed the trained model to reason the performance improvement, and we found
that adversarial dropout increases the sparsity of neural networks more than
the standard dropout does.Comment: submitted to AAAI-1
Pathology of C3 Glomerulopathy
C3 glomerulopathy is a renal disorder involving dysregulation of alternative pathway complement activation. In most instances, a membranoproliferative pattern of glomerular injury with a prevalence of C3 deposition is observed by immunofluorescence microscopy. Dense deposit disease (DDD) and C3 glomerulonephritis (C3GN) are subclasses of C3 glomerulopathy that are distinguishable by electron microscopy. Highly electron-dense transformation of glomerular basement membrane is characteristic of DDD. C3GN should be differentiated from post-infectious glomerulonephritis and other immune complex-mediated glomerulonephritides showing C3 deposits
Current Status of Image-Enhanced Endoscopy for Early Identification of Esophageal Neoplasms
Advanced esophageal cancer is known to have a poor prognosis. The early detection of esophageal neoplasms, including esophageal dysplasia and early esophageal cancer, is highly important for the accurate treatment of the disease. However, esophageal dysplasia and early esophageal cancer are usually subtle and can be easily missed. In addition to the early detection, proper pretreatment evaluation of the depth of invasion of esophageal cancer is very important for curative treatment. The progression of non-invasive diagnosis via image-enhanced endoscopy techniques has been shown to aid the early detection and estimate the depth of invasion of early esophageal cancer and, as a result, may provide additional opportunities for curative treatment. Here, we review the advancement of image-enhanced endoscopy-related technologies and their role in the early identification of esophageal neoplasms
- …