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Found 6 papers, 2 papers with code
Deep Co-supervision and Attention Fusion Strategy for Automatic COVID-19 Lung Infection Segmentation on CT Images
In this paper, a novel segmentation scheme is proposed for the infections of COVID-19 by enhancing supervised information and fusing multi-scale feature maps of different levels based on the encoder-decoder architecture.
Adaptively Customizing Activation Functions for Various Layers
To enhance the nonlinearity of neural networks and increase their mapping abilities between the inputs and response variables, activation functions play a crucial role to model more complex relationships and patterns in the data.
TaskDrop: A Competitive Baseline for Continual Learning of Sentiment Classification
In this paper, we study the multi-task sentiment classification problem in the continual learning setting, i. e., a model is sequentially trained to classifier the sentiment of reviews of products in a particular category.
Training Deep Neural Networks for Wireless Sensor Networks Using Loosely and Weakly Labeled Images
Although deep learning has achieved remarkable successes over the past years, few reports have been published about applying deep neural networks to Wireless Sensor Networks (WSNs) for image targets recognition where data, energy, computation resources are limited.
Residual-Recursion Autoencoder for Shape Illustration Images
Shape illustration images (SIIs) are common and important in describing the cross-sections of industrial products.
Towards the Automation of Deep Image Prior
Single image inverse problem is a notoriously challenging ill-posed problem that aims to restore the original image from one of its corrupted versions.
