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Found 8 papers, 2 papers with code
Perturbing the Gradient for Alleviating Meta Overfitting
However, there can be a number of novel solutions to this problem keeping in mind any of the two objectives to be attained, i. e. to increase diversity in the tasks and to reduce the confidence of the model for some of the tasks.
Equitable-FL: Federated Learning with Sparsity for Resource-Constrained Environment
We validate Equitable-FL on the $MNIST$, $F-MNIST$, and $CIFAR-10$ benchmark datasets, as well as the $Brain-MRI$ data and the $PlantVillage$ datasets.
FAM: fast adaptive federated meta-learning
There is a need to learn a global model that can be adapted using client's specific information to create personalized models on clients is required.
Deep Learning Techniques in Extreme Weather Events: A Review
Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact.
Learning to Learn with Indispensable Connections
We aim to perform two things: (a) to find a sub-network capable of more adaptive meta-learning and (b) to learn new low-level features of unseen tasks and recombine those features with the already learned features during the meta-test phase.
Hybrid Model using Feature Extraction and Non-linear SVM for Brain Tumor Classification
The method is threefold, in the first step, we normalize and resize the images, and the second step consists of feature extraction through variants of the VGG model.
Adaptive Prototypical Networks
Prototypical network for Few shot learning tries to learn an embedding function in the encoder that embeds images with similar features close to one another in the embedding space.
Parzen Window Approximation on Riemannian Manifold
In this paper, the bias due to uneven data sampling on the Riemannian manifold is catered to by a variable Parzen window determined as a function of neighborhood size, ambient dimension, flatness range, etc.
