Training Teachers is a Process Too

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Improving Workplace Expertise to Meet Increasing Customer Requirements: The Impact of Training

This article focuses upon the training of engineers at a factory producing integrated circuits. Inadequate use of statistical process techniques by the engineers meant that the production process was not being optimised in the context of increasing customer requirements. A training needs analysis was undertaken and a training programme was developed, implemented and evaluated. The results of this programme are presented and conclusions drawn

Modeling Surface Appearance from a Single Photograph using Self-augmented Convolutional Neural Networks

We present a convolutional neural network (CNN) based solution for modeling physically plausible spatially varying surface reflectance functions (SVBRDF) from a single photograph of a planar material sample under unknown natural illumination. Gathering a sufficiently large set of labeled training pairs consisting of photographs of SVBRDF samples and corresponding reflectance parameters, is a difficult and arduous process. To reduce the amount of required labeled training data, we propose to leverage the appearance information embedded in unlabeled images of spatially varying materials to self-augment the training process. Starting from an initial approximative network obtained from a small set of labeled training pairs, we estimate provisional model parameters for each unlabeled training exemplar. Given this provisional reflectance estimate, we then synthesize a novel temporary labeled training pair by rendering the exact corresponding image under a new lighting condition. After refining the network using these additional training samples, we re-estimate the provisional model parameters for the unlabeled data and repeat the self-augmentation process until convergence. We demonstrate the efficacy of the proposed network structure on spatially varying wood, metals, and plastics, as well as thoroughly validate the effectiveness of the self-augmentation training process.Comment: Accepted to SIGGRAPH 201

Reflections on the accreditation process: Advice for in-training practitioners

As professional and public interest in sport and exercise psychology continues to grow, so too the number of students enrolling on sport and exercise psychology courses is on the increase. As a result, the number of graduates looking to embark on a period of supervised experience within this domain is also expected to rise. The importance of practitioner training cannot be understated. It is intended to provide a standardised route to attaining accepted levels of competence in relation to knowledge, skills, and professional conduct. The accreditation process also doubles as a safeguard designed to ensure that the public can identify and are therefore protected from individuals practicing sport and exercise psychology who have not met accepted standards of professional competence. Given the necessity for such a process of training and accreditation, it is imperative that in-training practitioners are offered appropriate levels of information and support to ensure they are able to satisfy the specific criteria outlined by the relevant accreditation guidelines. Following the recent approval of the Society’s Stage 2 qualification in sport and exercise psychology, this article aims to provide current and aspiring in-training practitioners with helpful tips and advice regarding the accreditation process, outlining some of the key considerations that will help individuals successfully navigate the various obstacles they must overcome

Low-Shot Learning with Imprinted Weights

Human vision is able to immediately recognize novel visual categories after seeing just one or a few training examples. We describe how to add a similar capability to ConvNet classifiers by directly setting the final layer weights from novel training examples during low-shot learning. We call this process weight imprinting as it directly sets weights for a new category based on an appropriately scaled copy of the embedding layer activations for that training example. The imprinting process provides a valuable complement to training with stochastic gradient descent, as it provides immediate good classification performance and an initialization for any further fine-tuning in the future. We show how this imprinting process is related to proxy-based embeddings. However, it differs in that only a single imprinted weight vector is learned for each novel category, rather than relying on a nearest-neighbor distance to training instances as typically used with embedding methods. Our experiments show that using averaging of imprinted weights provides better generalization than using nearest-neighbor instance embeddings.Comment: CVPR 201

The use of process selection exercises for the training of welding technologists

Formal lectures and laboratory experiments do not give students sufficient experience of welding processes applied to specific fabrications. A series of designs has been evolved which ib.capable of being fabricated by several different methods and students are required to produce fabrication procedures for each design. Suggested procedures are then discussed in an open forum in which both staff and students participate. The use of these exercises has been found to be a useful method whereby course members can pass on their own experience to other students and in which students learn to make decisions based on available, but often incomplete, facts