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Found 8 papers, 4 papers with code
PI-RADS v2 Compliant Automated Segmentation of Prostate Zones Using co-training Motivated Multi-task Dual-Path CNN
Further, the representations from different branches act complementary to each other at the second stage of training, where they are fine-tuned through an unsupervised loss.
Improving Voice Conversion for Dissimilar Speakers Using Perceptual Losses
The rising trend of using voice as a means of interacting with smart devices has sparked worries over the protection of users' privacy and data security.
StarGAN-VC++: Towards Emotion Preserving Voice Conversion Using Deep Embeddings
In this paper, we show that StarGANv2-VC fails to disentangle the speaker and emotion representations, pertinent to preserve emotion.
Emo-StarGAN: A Semi-Supervised Any-to-Many Non-Parallel Emotion-Preserving Voice Conversion
Speech anonymisation prevents misuse of spoken data by removing any personal identifier while preserving at least linguistic content.
Dual Branch Prior-SegNet: CNN for Interventional CBCT using Planning Scan and Auxiliary Segmentation Loss
This paper proposes an extension to the Dual Branch Prior-Net for sparse view interventional CBCT reconstruction incorporating a high quality planning scan.
Predictive coding, precision and natural gradients
Here we focus on the related, but still largely under-explored connection between precision weighting in predictive coding networks and the Natural Gradient Descent algorithm for deep neural networks.
Uncertainty-Aware Temporal Self-Learning (UATS): Semi-Supervised Learning for Segmentation of Prostate Zones and Beyond
Various convolutional neural network (CNN) based concepts have been introduced for the prostate's automatic segmentation and its coarse subdivision into transition zone (TZ) and peripheral zone (PZ).
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors.
