Such Swapped errors are also common vehicles, object detection remains fairly in many classification datasets (Northcutt et al., 2021a), brittle in part due to annotation errors that plague but the increased complexity of object detection annotation most real-world training datasets. We propose introduces potential for more varied types of label errors ObjectLab, a straightforward algorithm to detect than encountered in classification. We propose an algorithm, diverse errors in object detection labels, including: ObjectLab, that utilizes any trained object detection model overlooked bounding boxes, badly located boxes, to estimate the incorrect labels in such a dataset, regardless and incorrect class label assignments. Object-which of these 3 types of mistake the data annotators made. Lab utilizes any trained object detection model to score the label quality of each image, such that Training and evaluating models with incorrect bounding box mislabeled images can be automatically prioritized annotations is clearly worrisome.

In multi-label classification, each example in a dataset may be annotated as belonging to one or more classes (or none of the classes). Example applications include image (or document) tagging where each possible tag either applies to a particular image (or document) or not. With many possible classes to consider, data annotators are likely to make errors when labeling such data in practice. Here we consider algorithms for finding mislabeled examples in multi-label classification datasets. We propose an extension of the Confident Learning framework to this setting, as well as a label quality score that ranks examples with label errors much higher than those which are correctly labeled. Both approaches can utilize any trained classifier. After demonstrating that our methodology empirically outperforms other algorithms for label error detection, we apply our approach to discover many label errors in the CelebA image tagging dataset.

We present a siamese adaptation of the Long Short-Term Memory (LSTM) network for labeled data comprised of pairs of variable-length sequences. Our model is applied to assess semantic similarity between sentences, where we exceed state of the art, outperforming carefully handcrafted features and recently proposed neural network systems of greater complexity. For these applications, we provide word-embedding vectors supplemented with synonymic information to the LSTMs, which use a fixed size vector to encode the underlying meaning expressed in a sentence (irrespective of the particular wording/syntax). By restricting subsequent operations to rely on a simple Manhattan metric, we compel the sentence representations learned by our model to form a highly structured space whose geometry reflects complex semantic relationships. Our results are the latest in a line of findings that showcase LSTMs as powerful language models capable of tasks requiring intricate understanding.