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Self-Supervised Vessel Enhancement Using Flow-Based Consistencies
Vessel segmenting is an essential task in many clinical applications. Al...
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Explaining the Black-box Smoothly- A Counterfactual Approach
We propose a BlackBox Counterfactual Explainer that is explicitly develo...
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Semi-Supervised Hierarchical Drug Embedding in Hyperbolic Space
Learning accurate drug representation is essential for tasks such as com...
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Explanation by Progressive Exaggeration
As machine learning methods see greater adoption and implementation in h...
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Robust Ordinal VAE: Employing Noisy Pairwise Comparisons for Disentanglement
Recent work by Locatello et al. (2018) has shown that an inductive bias ...
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Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach
Majority of state-of-the-art monocular depth estimation methods are supe...
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Twin Auxiliary Classifiers GAN
Conditional generative models enjoy remarkable progress over the past fe...
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Weakly Supervised Disentanglement by Pairwise Similarities
Recently, researches related to unsupervised disentanglement learning wi...
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Generative-Discriminative Complementary Learning
Majority of state-of-the-art deep learning methods for vision applicatio...
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Robust Angular Local Descriptor Learning
In recent years, the learned local descriptors have outperformed handcra...
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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Gliomas are the most common primary brain malignancies, with different d...
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Deep Diffeomorphic Normalizing Flows
The Normalizing Flow (NF) models a general probability density by estima...
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Geometry-Consistent Adversarial Networks for One-Sided Unsupervised Domain Mapping
Unsupervised domain mapping aims at learning a function to translate dom...
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Deep Ordinal Regression Network for Monocular Depth Estimation
Monocular depth estimation, which plays a crucial role in understanding ...
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Causal Generative Domain Adaptation Networks
We propose a new generative model for domain adaptation, in which traini...
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Causal Discovery in the Presence of Measurement Error: Identifiability Conditions
Measurement error in the observed values of the variables can greatly ch...
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Nonparametric Spherical Topic Modeling with Word Embeddings
Traditional topic models do not account for semantic regularities in lan...
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