means being highly related to my personal research interest.
Example Weighting by Meta Learning=> Gradient Directions
Learning to Reweight Examples for Robust Deep Learning
- A novel meta-learning algorithm that learns to assign weights to training examples based on their gradient directions. To determine the example weights, our method performs a meta gradient descent step on the current mini-batch example weights (which are initialized from zero) to minimize the loss on a clean unbiased validation set.
- There exist two contradicting ideas in training loss based approaches. In noisy label problems, we prefer examples with smaller training losses as they are more likely to be clean images; yet in class imbalance problems, algorithms such as hard negative mining (Malisiewicz et al., 2011) prioritize examples with higher training loss since they are more likely to be the minority class. In cases when the training set is both imbalanced and noisy, these existing methods would have the wrong model assumptions.
- We follow a meta-learning paradigm and model the most basic assumption instead: the best example weighting should minimize the loss of a set of unbiased clean validation examples that are consistent with the evaluation procedure.
- Suppose that a pair of training and validation examples are very similar, and they also provide similar gradient directions, then this training example is helpful and should be up-weighted, and conversely, if they provide opposite gradient directions, this training example is harmful and should be downweighed.