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Found 5 papers, 1 papers with code
Improving Meta-Learning Generalization with Activation-Based Early-Stopping
Specifically, we analyze the evolution, during meta-training, of the neural activations at each hidden layer, on a small set of unlabelled support examples from a single task of the target tasks distribution, as this constitutes a minimal and justifiably accessible information from the target problem.
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers
Empirical science of neural scaling laws is a rapidly growing area of significant importance to the future of machine learning, particularly in the light of recent breakthroughs achieved by large-scale pre-trained models such as GPT-3, CLIP and DALL-e.
Early-Stopping for Meta-Learning: Estimating Generalization from the Activation Dynamics
To this end, we empirically show that as meta-training progresses, a model's generalization to a target distribution of novel tasks can be estimated by analysing the dynamics of its neural activations.
Towards an Unsupervised Method for Model Selection in Few-Shot Learning
The study of generalization of neural networks in gradient-based meta-learning has recently great research interest.
Towards Understanding Generalization in Gradient-Based Meta-Learning
We also show that coherence of meta-test gradients, measured by the average inner product between the task-specific gradient vectors evaluated at meta-train solution, is also correlated with generalization.
