When learning a new concept, not all training examples may prove equally useful for training: some may have higher or lower training value than others. The goal of this paper is to bring to the attention of the vision community the following considerations: (1) some examples are better than others for training detectors or classifiers, and (2) in the presence of better examples, some examples may negatively impact performance and removing them may be beneficial. In this paper, we propose an approach for measuring the training value of an example, and use it for ranking and greedily sorting examples. We test our methods on different vision tasks, models, datasets and classifiers. Our experiments show that the performance of current state-of-the-art detectors and classifiers can be improved when training on a subset, rather than the whole training set.
@cite_5: Training datasets for learning of object categories are often contaminated or imperfect. We explore an approach to automatically identify examples that are noisy or troublesome for learning and exclude them from the training set. The problem is relevant to learning in semi-supervised or unsupervised setting, as well as to learning when the training data is contaminated with wrongly labeled examples or when correctly labeled, but hard to learn examples, are present. We propose a fully automatic mechanism for noise cleaning, called 'data pruning' and demonstrate its success on learning of human faces. It is not assumed that the data or the noise can be modeled or that additional training examples are available. Our experiments show that data pruning can improve on generalization performance for algorithms with various robustness to noise. It outperforms methods with regularization properties and is superior to commonly applied aggregation methods, such as bagging.
Although training with the full training set is common practice, there are a few notable exceptions. @cite_5 propose a method of pruning a training set and show its effectiveness in removing outliers and hard-to-learn examples. Within the context of a face recognition task, they introduce outliers (background samples labeled as faces) to the training set to test their algorithm. They train multiple classifiers using different partitions of the training set and identify which examples create more disagreement across classifiers, labeling them as troublesome. In our experiments we do not introduce any artificial noise because we can show that current datasets contain a large number of hard-to-learn examples for current recognition models, and prunning them can be beneficial.