In many applications of machine learning — including image recognition (Krizhevsky, Sutskever, and Hinton 2012), machine translation (Sutskever, Vinyals, and Le
2014), and speech recognition (Graves, Mohamed, and Hinton 2013) — large labeled data sets are a key component for
building state-of-the-art systems. Collecting such data sets
can be expensive, representing a major bottleneck in deploying machine learning algorithms.
Humans, on the other hand, are able to learn most tasks
without direct examples, opting instead for high-level
instructions for how each task should be performed, or what
it will look like when completed. In this work, we ask
whether a similar principle can be applied to teaching
machines: can we supervise algorithms with a few (or no)
labeled examples by instead only describing how desired outputs should look or by giving a small set of examples of outputs?
Learning with Weak Supervision from
Physics and Data-Driven Constraints
Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon
; In many applications of machine
learning, labeled data is scarce and
obtaining additional labels is expensive.
We introduce a new approach to supervising learning algorithms without
labels by enforcing a small number of
domain-specific constraints over the
algorithms’ outputs. The constraints
can be provided explicitly based on prior knowledge — for example, we may
require that objects detected in videos
satisfy the laws of physics — or implicitly extracted from data using a novel
framework inspired by adversarial
training. We demonstrate the effectiveness of constraint-based learning on a
variety of tasks — including tracking,
object detection, and human pose estimation — and we find that algorithms
supervised with constraints achieve
high accuracies with only a small number of labels, or with no labels at all in
some cases.
