Medical image analysis
Feb 13, 2022
Existing skin attributes detection methods usually initialize with a pre-trained Imagenet network and then fine-tune on a medical target task. However, we argue that such approaches are suboptimal because medical datasets are largely different from I...
Sensors (Basel, Switzerland)
Feb 11, 2022
Cross-domain decision-making systems are suffering a huge challenge with the rapidly emerging uneven quality of user-generated data, which poses a heavy responsibility to online platforms. Current content analysis methods primarily concentrate on non...
The Journal of experimental biology
Feb 10, 2022
Birdsong learning has been consolidated as the model system of choice for exploring the biological substrates of vocal learning. In the zebra finch (Taeniopygia guttata), only males sing and they develop their song during a sensitive period in early ...
Sensors (Basel, Switzerland)
Feb 9, 2022
Deep neural networks can learn powerful representations from massive amounts of labeled data; however, their performance is unsatisfactory in the case of large samples and small labels. Transfer learning can bridge between a source domain with rich s...
Computational intelligence and neuroscience
Feb 8, 2022
Visual object tracking is an important topic in computer vision, which has successfully utilized pretrained convolutional neural networks, such as VGG and ResNet. However, the features extracted by these pretrained models are high dimensional, and th...
Sensors (Basel, Switzerland)
Feb 7, 2022
As one of the most effective methods of vulnerability mining, fuzzy testing has scalability and complex path detection ability. Fuzzy testing sample generation is the key step of fuzzy testing, and the quality of sample directly determines the vulner...
IEEE transactions on pattern analysis and machine intelligence
Feb 3, 2022
Graph convolutional networks (GCNs) have well-documented performance in various graph learning tasks, but their analysis is still at its infancy. Graph scattering transforms (GSTs) offer training-free deep GCN models that extract features from graph ...
IEEE transactions on pattern analysis and machine intelligence
Feb 3, 2022
Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few...
Neuron
Jan 31, 2022
How do neural populations code for multiple, potentially conflicting tasks? Here we used computational simulations involving neural networks to define "lazy" and "rich" coding solutions to this context-dependent decision-making problem, which trade o...
Neural networks : the official journal of the International Neural Network Society
Jan 29, 2022
Many approaches in generalized zero-shot learning (GZSL) rely on cross-modal mapping between the image feature space and the class embedding space, which achieves knowledge transfer from seen to unseen classes. However, these two spaces are completel...