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Found 10 papers, 3 papers with code
Surface Realization Using Pretrained Language Models
In the context of Natural Language Generation, surface realization is the task of generating the linear form of a text following a given grammar.
Source-free Domain Adaptation Requires Penalized Diversity
In addition, PD is augmented with a weighted MI maximization objective for label distribution shift.
on the effectiveness of generative adversarial network on anomaly detection
Identifying anomalies refers to detecting samples that do not resemble the training data distribution.
Boosting Segmentation Performance across datasets using histogram specification with application to pelvic bone segmentation
With the emergence and advancements of deep learning for digital healthcare, several methodologies have been proposed for such segmentation tasks.
FoCL: Feature-Oriented Continual Learning for Generative Models
In this paper, we propose a general framework in continual learning for generative models: Feature-oriented Continual Learning (FoCL).
The Concordia NLG Surface Realizer at SRST 2019
This paper presents the model we developed for the shallow track of the 2019 NLG Surface Realization Shared Task.
The TCGA Meta-Dataset Clinical Benchmark
Machine learning is bringing a paradigm shift to healthcare by changing the process of disease diagnosis and prognosis in clinics and hospitals.
Dual Adversarial Inference for Text-to-Image Synthesis
), and the style, which is usually not well described in the text (e. g., location, quantity, size, etc.).
Case-Based Histopathological Malignancy Diagnosis using Convolutional Neural Networks
In practice, histopathological diagnosis of tumor malignancy often requires a human expert to scan through histopathological images at multiple magnification levels, after which a final diagnosis can be accurately determined.
Leveraging Disease Progression Learning for Medical Image Recognition
Unlike natural images, medical images often have intrinsic characteristics that can be leveraged for neural network learning.
