# BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise

arXiv: https://arxiv.org/abs/2309.06046 · HTML: https://mlsanity.com/paper/2309.06046/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 25 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 19 of 19)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Learning Representations by Maximizing Mutual Information Across Views](https://mlsanity.com/paper/1906.00910/) (2019)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [Meta-Learning with Implicit Gradients](https://mlsanity.com/paper/1909.04630/) (2019)
- [Meta-learning with differentiable closed-form solvers](https://mlsanity.com/paper/1805.08136/) (2018)
- [Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks](https://mlsanity.com/paper/1903.11680/) (2019)
- [Decoupled Contrastive Learning](https://mlsanity.com/paper/2110.06848/) (2021)
- [Unsupervised Learning via Meta-Learning](https://mlsanity.com/paper/1810.02334/) (2018)
- [Unsupervised Meta-Learning For Few-Shot Image Classification](https://mlsanity.com/paper/1811.11819/) (2018)
- [Meta-learning with an Adaptive Task Scheduler](https://mlsanity.com/paper/2110.14057/) (2021)
- [Self-Supervised Prototypical Transfer Learning for Few-Shot Classification](https://mlsanity.com/paper/2006.11325/) (2020)
- [Few-shot Learning with Noisy Labels](https://mlsanity.com/paper/2204.05494/) (2022)
- [RNNP: A Robust Few-Shot Learning Approach](https://mlsanity.com/paper/2011.11067/) (2020)
- [MAML is a Noisy Contrastive Learner in Classification](https://mlsanity.com/paper/2106.15367/) (2021)
- [Diversity vs. Recognizability: Human-like generalization in one-shot generative models](https://mlsanity.com/paper/2205.10370/) (2022)
- [A Nested Bi-level Optimization Framework for Robust Few Shot Learning](https://mlsanity.com/paper/2011.06782/) (2020)
- [Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile](https://mlsanity.com/paper/2206.01944/) (2022)
- [Self-Attention Message Passing for Contrastive Few-Shot Learning](https://mlsanity.com/paper/2210.06339/) (2022)

## Abstract (arXiv)

The negative impact of label noise is well studied in classical supervised learning yet remains an open research question in meta-learning. Meta-learners aim to adapt to unseen learning tasks by learning a good initial model in meta-training and consecutively fine-tuning it according to new tasks during meta-testing. In this paper, we present the first extensive analysis of the impact of varying levels of label noise on the performance of state-of-the-art meta-learners, specifically gradient-based N-way K-shot learners. We show that the accuracy of Reptile, iMAML, and foMAML drops by up to 42% on the Omniglot and CifarFS datasets when meta-training is affected by label noise. To strengthen the resilience against label noise, we propose two sampling techniques, namely manifold (Man) and batch manifold (BatMan), which transform the noisy supervised learners into semi-supervised ones to increase the utility of noisy labels. We first construct manifold samples of N-way 2-contrastive-shot tasks through augmentation, learning the embedding via a contrastive loss in meta-training, and then perform classification through zeroing on the embedding in meta-testing. We show that our approach can effectively mitigate the impact of meta-training label noise. Even with 60% wrong labels batman and man can limit the meta-testing accuracy drop to 2.5, 9.4, 1.1 percent points, respectively, with existing meta-learners across the Omniglot, CifarFS, and MiniImagenet datasets.