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Found 9 papers, 1 papers with code
One Category One Prompt: Dataset Distillation using Diffusion Models
However, traditional dataset distillation approaches often struggle to scale effectively with high-resolution images and more complex architectures due to the limitations in bi-level optimization.
Dynamic Online Modulation Recognition using Incremental Learning
This issue renders DL-based modulation recognition models inapplicable in real-world scenarios because the dynamic nature of communication systems necessitate the effective adaptability to new modulation schemes.
CovarNav: Machine Unlearning via Model Inversion and Covariance Navigation
Given a trained model and a subset of training data designated to be forgotten (i. e., the "forget set"), we introduce a three-step process, named CovarNav, to facilitate this forgetting.
BrainWash: A Poisoning Attack to Forget in Continual Learning
Continual learning has gained substantial attention within the deep learning community, offering promising solutions to the challenging problem of sequential learning.
Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks
Direct sharing of the data distribution may be prohibitive due to the additional private information that is sent from the clients.
PRANC: Pseudo RAndom Networks for Compacting deep models
We demonstrate that a deep model can be reparametrized as a linear combination of several randomly initialized and frozen deep models in the weight space.
Sparsity and Heterogeneous Dropout for Continual Learning in the Null Space of Neural Activations
Overcoming catastrophic forgetting in deep neural networks has become an active field of research in recent years.
Teaching Networks to Solve Optimization Problems
In addition, we propose two alternative approaches for learning such parametric functions, with and without a solver in the LOOP.
Identity-Preserving Pose-Robust Face Hallucination Through Face Subspace Prior
Over the past few decades, numerous attempts have been made to address the problem of recovering a high-resolution (HR) facial image from its corresponding low-resolution (LR) counterpart, a task commonly referred to as face hallucination.
