Our Projects

1. Visual category learning by toddlers provides new principles for teaching rapid generalization

(NSF grant BCS-1842817) with David Crandall

The crux of both human and machine learning is generalization: how can a learning system, biological or artificial, perform well not only on its training examples but also on novel examples and circumstances? One approach, widely used and well supported in both human and machine learning is experience with many training examples. This solution avoids “overfitting” but is slow and incremental. However, in some cases of human learning, generalization requires minimal experience. Evidence of rapid learning from few examples, often called “one-shot” or “few-shot” is particularly well documented in learning visual objects as well as scientific and mathematical concepts. Incremental and one-shot learning have been discussed as distinct mechanisms, but there is growing interest in how one-shot learning might emerge out of prior incremental learning, an idea related to the broader concept of “learning to learn”. The central idea explaining rapid learning from minimal examples is that deep representational principles allow the learner to represent novel examples for appropriate generalization. Thus, most research on one-shot learning – experimental and computational – focuses on the nature of these representations or on the learning machinery. But if one-shot learning is learnable, then an additional core question concerns the kinds of experiencesthat teach an incremental learner to become a one-shot learner. This is our focus. Our main idea is that generalization depends on knowing the allowable and not allowable transformations, for example, the allowable transformations for different views of the same object, for membership in a category, for indicating the same (as in 3-1 and 1+1).  We seek to:

  1. characterize the transformations,
  2. in time,
  3. their active generation through behavior,
  4. the underlying learning (and memory) mechanisms.

2. Infants' self-generated visual statistics support object and category learning

(NIH-NICHD R01HD104624) with Jim Rehg, Chen Yu, and David Crandall

Human visual object recognition is characterized by two remarkable competencies. The first, Recognition, concerns the perception of an individual object as the same thing despite the variability in the 2D image of the object projected to the eye. The second competency, Categorization, is the recognition of never-seen-before things as members of categories -- as dogs, cups, chairs and flowers. The field does not have a unified understanding of these two competencies nor their developmental origins.  Between the period of 18 to 24 months, visual object Recognition and Categorization show marked advances. This project pursues the developmental ties between the two achievements as driven by toddlers’ extended visual experiences with individual instances of a category.  Our hypothesis is that category learning (and one-shot learning) in humans derives from extended (and unlabeled) visual experiences with individual objects and not by learning about the many different objects in the same category.