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Found 6 papers, 4 papers with code
Prediction-Guided Distillation for Dense Object Detection
Based on this, we propose Prediction-Guided Distillation (PGD), which focuses distillation on these key predictive regions of the teacher and yields considerable gains in performance over many existing KD baselines.
How Sensitive are Meta-Learners to Dataset Imbalance?
Meta-Learning (ML) has proven to be a useful tool for training Few-Shot Learning (FSL) algorithms by exposure to batches of tasks sampled from a meta-dataset.
Few-Shot Learning with Class Imbalance
Few-Shot Learning (FSL) algorithms are commonly trained through Meta-Learning (ML), which exposes models to batches of tasks sampled from a meta-dataset to mimic tasks seen during evaluation.
Class Imbalance in Few-Shot Learning
Few-shot learning aims to train models on a limited number of labeled samples from a support set in order to generalize to unseen samples from a query set.
A Comparison of Few-Shot Learning Methods for Underwater Optical and Sonar Image Classification
Deep convolutional neural networks generally perform well in underwater object recognition tasks on both optical and sonar images.
Defining Benchmarks for Continual Few-Shot Learning
Both few-shot and continual learning have seen substantial progress in the last years due to the introduction of proper benchmarks.
