Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations

Images seen during test time are often not from the same distribution as images used for learning. This problem, known as domain shift, occurs when training classifiers from object-centric internet image databases and trying to apply them directly to scene understanding tasks. The consequence is often severe performance degradation and is one of the major barriers for the application of classifiers in real-world systems. In this paper, we show how to learn transform-based domain adaptation classifiers in a scalable manner. The key idea is to exploit an implicit rank constraint, originated from a max-margin domain adaptation formulation, to make optimization tractable. Experiments show that the transformation between domains can be very efficiently learned from data and easily applied to new categories. This begins to bridge the gap between large-scale internet image collections and object images captured in everyday life environments

Transfer learning by borrowing examples for multiclass object detection

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 31-33).Despite the recent trend of increasingly large datasets for object detection, there still exist many classes with few training examples. To overcome this lack of training data for certain classes, we propose a novel way of augmenting the training data for each class by borrowing and transforming examples from other classes. Our model learns which training instances from other classes to borrow and how to transform the borrowed examples so that they become more similar to instances from the target class. Our experimental results demonstrate that our new object detector, with borrowed and transformed examples, improves upon the current state-of-the-art detector on the challenging SUN09 object detection dataset.by Joseph J. Lim.S.M

Visual Transfer Learning: Informal Introduction and Literature Overview

Transfer learning techniques are important to handle small training sets and to allow for quick generalization even from only a few examples. The following paper is the introduction as well as the literature overview part of my thesis related to the topic of transfer learning for visual recognition problems.Comment: part of my PhD thesi

Inferring Analogous Attributes

The appearance of an attribute can vary considerably from class to class (e.g., a “fluffy ” dog vs. a “fluffy ” towel), making standard class-independent attribute models break down. Yet, training object-specific models for each at-tribute can be impractical, and defeats the purpose of us-ing attributes to bridge category boundaries. We propose a novel form of transfer learning that addresses this dilemma. We develop a tensor factorization approach which, given a sparse set of class-specific attribute classifiers, can in-fer new ones for object-attribute pairs unobserved during training. For example, even though the system has no la-beled images of striped dogs, it can use its knowledge of other attributes and objects to tailor “stripedness ” to the dog category. With two large-scale datasets, we demon-strate both the need for category-sensitive attributes as well as our method’s successful transfer. Our inferred attribute classifiers perform similarly well to those trained with the luxury of labeled class-specific instances, and much better than those restricted to traditional modes of transfer. 1