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Found 6 papers, 4 papers with code
TiDAL: Learning Training Dynamics for Active Learning
Active learning (AL) aims to select the most useful data samples from an unlabeled data pool and annotate them to expand the labeled dataset under a limited budget.
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection
Recent studies on learning with noisy labels have shown remarkable performance by exploiting a small clean dataset.
Meta-Learned Confidence for Transductive Few-shot Learning
A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples, or confidence-weighted average of all the query samples.
Improving Multi-Scale Aggregation Using Feature Pyramid Module for Robust Speaker Verification of Variable-Duration Utterances
In this approach, we obtain a speaker embedding vector by pooling single-scale features that are extracted from the last layer of a speaker feature extractor.
Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs
By combining these two learning schemes, our model outperforms existing state-of-the-art speaker verification models learned with a standard supervised learning framework on short utterance (1-2 seconds) on the VoxCeleb datasets.
Meta-Learned Confidence for Few-shot Learning
To tackle this issue, we propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries such that they improve the model's transductive inference performance on unseen tasks.
