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Found 7 papers, 1 papers with code
Leveraging Different Learning Styles for Improved Knowledge Distillation
Unlike conventional techniques that share the same type of knowledge with all networks, we propose to train individual networks with different forms of information to enhance the learning process.
IPNET:Influential Prototypical Networks for Few Shot Learning
Further, the influence factor of a sample is measured using MMD based on the shift in the distribution in the absence of that sample.
Influential Prototypical Networks for Few Shot Learning: A Dermatological Case Study
Conventional PN attributes equal importance to all samples and generates prototypes by simply averaging the support sample embeddings belonging to each class.
Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks
The proposed method was evaluated on a benchmark medical imaging dataset with image reconstruction as the self-supervised task and segmentation as the image analysis task.
Augmenting Knowledge Distillation With Peer-To-Peer Mutual Learning For Model Compression
Knowledge distillation (KD) is an effective model compression technique where a compact student network is taught to mimic the behavior of a complex and highly trained teacher network.
MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Disease
Medical imaging datasets are inherently high dimensional with large variability and low sample sizes that limit the effectiveness of deep learning algorithms.
EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification
Different categories of visual stimuli activate different responses in the human brain.
