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

Transfer learning for image classification with sparse prototype representations

To learn a new visual category from few examples, prior knowledge from unlabeled data as well as previous related categories may be useful.  We develop a new method for transfer learning which exploits available unlabeled data and an arbitrary kernel function; we form a representation based on kernel distances to a large set of unlabeled data points. To transfer knowledge from previous related problems we observe that a category might be learnable using only a small subset of reference prototypes. Related problems may share a significant number of relevant prototypes; we find such a reduced representation by performing a joint loss minimization over the training sets of related problems with a shared regularization penalty that minimizes the total number of prototypes involved in the approximation.This optimization problem can be formulated as a linear program thatcan be solved efficiently. We conduct experiments on a news-topic prediction task where the goal is to predict whether an image belongs to a particularnews topic. Our results show that when only few examples are available for training a target topic, leveraging knowledge learnt from other topics can significantly improve performance

Bootstrapping for text learning tasks

Journal ArticleWhen applying text learning algorithms to complex tasks, it is tedious and expensive to hand-label the large amounts of training data necessary for good performance. This paper presents bootstrapping as an alternative approach to learning from large sets of labeled data. Instead of a large quantity of labeled data, this paper advocates using a small amount of seed information and a large collection of easily-obtained unlabeled data. Bootstrapping initializes a learner with the seed information; it then iterates, applying the learner to calculate labels for the unlabeled data, and incorporating some of these labels into the training input for the learner. Two case studies of this approach are presented. Bootstrapping for information extraction provides 76% precision for a 250-word dictionary for extracting locations from web pages, when starting with just a few seed locations. Bootstrapping a text classifier from a few keywords per class and a class hierarchy provides accuracy of 66%, a level close to human agreement, when placing computer science research papers into a topic hierarchy. The success of these two examples argues for the strength of the general boot¬ strapping approach for text learning tasks

PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization

Learning neural networks using only few available information is an important ongoing research topic with tremendous potential for applications. In this paper, we introduce a powerful regularizer for the variational modeling of inverse problems in imaging. Our regularizer, called patch normalizing flow regularizer (patchNR), involves a normalizing flow learned on small patches of very few images. In particular, the training is independent of the considered inverse problem such that the same regularizer can be applied for different forward operators acting on the same class of images. By investigating the distribution of patches versus those of the whole image class, we prove that our model is indeed a MAP approach. Numerical examples for low-dose and limited-angle computed tomography (CT) as well as superresolution of material images demonstrate that our method provides very high quality results. The training set consists of just six images for CT and one image for superresolution. Finally, we combine our patchNR with ideas from internal learning for performing superresolution of natural images directly from the low-resolution observation without knowledge of any high-resolution image

The role of re-appropriation in open design : a case study on how openness in higher education for industrial design engineering can trigger global discussions on the theme of urban gardening

This case study explores the opportunities for students of Industrial Design Engineering to engage with direct and indirect stakeholders by making their design process and results into open-ended Designed Solutions. The reported case study involved 47 students during a two-weeks intensive course on the topic of urban gardening. Observations were collected during three distinctive phases: the co-design phase, the creation of an Open Design and the sharing of these design solutions on the online platform Instructables.com. The open sharing of local solutions triggered more global discussions, based on several types of feedbacks: from simple questions to reference to existing works and from suggestions to critiques. Also some examples of re-appropriation of the designed solutions were reported. These feedbacks show the possibilities for students to have a global vision on their local solutions, confronting them with a wider and more diverse audience. The case study shows on the other hand the difficulty in keeping students engaged in this global discussion, considering how after a few weeks the online discussions dropped to an almost complete silence. It is also impossible with such online platforms to follow the re-appropriation cycles, losing the possibility of exploring the new local context were the replication / modification of the designed product occurred. The course’s focus on Open Design is interesting both under the design and educational points of view. It implies a deep change in the teaching approach and learning attitude of students, allowing unknown peers to take part of the design process and fostering a global discussion starting from unique and local solutions

Dioramas as a Place for Play and Early Science Learning: Exploring Teachers’ Perspectives and Experiences

In this qualitative study, we explore teachers’ perspectives and experiences with play and learning at dioramas as few studies on this topic exist. In a time when play is disappearing from classrooms (Nicolopoulou, 2010), scholars advocate for a return to play-based learning (Miller & Almon, 2009). Using grounded theory (Charmaz, 2006), we inquired: 1) How do teachers describe the ways in which children play and learn with dioramas during their classes?, 2) What do teachers perceive as the affordances and opportunities that dioramas provide for children’s play and learning?, and 3) What strategies and pedagogical decisions do teachers make to promote play and learning at diorama? We interviewed ten early childhood educators who teach at a large, urban museum. Nearly 30 unique examples of play and learning with dioramas were provided, nine referenced by multiple teachers. Findings suggest that play and learning at or inspired by dioramas looks different across classes and contexts, but is perceived as vital in sparking imagination and creativity for young children when integrated into experiences and affords unique opportunities. This study highlights how dioramas can be integral in play-based science learning—making museums that are not traditionally designed for children into places for play