Context Embedding Networks
This work improves interpretable embeddings for applications relying on crowd data, but it is incremental as it builds on existing embedding methods by adding context and bias modeling.
The paper tackled the problem of learning low-dimensional embeddings from crowd-sourced similarity judgments by addressing limitations of existing models, such as ignoring individual differences and visual context, and showed that modeling worker bias and visual context leads to more interpretable embeddings on two noisy datasets.
Low dimensional embeddings that capture the main variations of interest in collections of data are important for many applications. One way to construct these embeddings is to acquire estimates of similarity from the crowd. However, similarity is a multi-dimensional concept that varies from individual to individual. Existing models for learning embeddings from the crowd typically make simplifying assumptions such as all individuals estimate similarity using the same criteria, the list of criteria is known in advance, or that the crowd workers are not influenced by the data that they see. To overcome these limitations we introduce Context Embedding Networks (CENs). In addition to learning interpretable embeddings from images, CENs also model worker biases for different attributes along with the visual context i.e. the visual attributes highlighted by a set of images. Experiments on two noisy crowd annotated datasets show that modeling both worker bias and visual context results in more interpretable embeddings compared to existing approaches.