Unsupervised Multi-label Dataset Generation from Web Data
This addresses the need for scalable, unsupervised dataset creation for multi-label classification tasks, though it is incremental in building upon existing unsupervised methods.
The paper tackles the problem of generating multi-label datasets from web data without supervision, achieving 85% correct labeling in a single-label dataset and adding 9.5% to 27% extra labels in multi-label augmentation.
This paper presents a system towards the generation of multi-label datasets from web data in an unsupervised manner. To achieve this objective, this work comprises two main contributions, namely: a) the generation of a low-noise unsupervised single-label dataset from web-data, and b) the augmentation of labels in such dataset (from single label to multi label). The generation of a single-label dataset uses an unsupervised noise reduction phase (clustering and selection of clusters using anchors) obtaining a 85% of correctly labeled images. An unsupervised label augmentation process is then performed to assign new labels to the images in the dataset using the class activation maps and the uncertainty associated with each class. This process is applied to the dataset generated in this paper and a public dataset (Places365) achieving a 9.5% and 27% of extra labels in each dataset respectively, therefore demonstrating that the presented system can robustly enrich the initial dataset.