We visit the a bottling line, where streams of Blantons Burbon are being hand labelled, waxed, and dropped into boxes.
Hand labeling large-scale datasets is tedious and time consuming; thus a more efficient labeling method would be beneficial.
Consider special education for children: On the one hand, labeling a child "disabled" risks stigmatizing and isolating the child.
On the other hand, labeled text data is often more available than labeled image data and text features have more semantic meaning for predicting a class label.
In their method, a negligible percentage (e.g., 3%) of faces need to be hand labeled, while the rest of the faces are automatically marked.
On the other hand, labeling of immune cells serves as an ideal tool for visualization, diagnosis or treatment of inflammatory processes, which requires the efficient internalization of the nanoparticles into the cells of interest.
However, a dilemma we have to face is that, on the one hand, the labeled patterns play a pivotal role for the learning algorithms; but on the other hand, labeling patterns is often expensive in terms of the computation time or cost, particularly for the large-scale streaming data, which is intractable.
The MSRC-21 dataset contains 591 images hand labeled with 21 classes: building, grass, tree, cow, sheep, sky, airplane, water, face, car, bicycle, flower, sign, bird, book, chair, road, cat, dog, body, and boat.
