title: Using swarm optimization to enhance autoencoder’s images

authors: Doaud, Maisa

subjects: computer science

abstract: Autoencoders learn data representations through reconstruction. Robust training is the key factor affecting the quality of the learned representations and, consequently, the accuracy of the application that use them. Previous works suggested methods for deciding the optimal autoencoder configuration which allows for robust training. Nevertheless, improving the accuracy of a trained autoencoder has got limited, if no, attention. We propose a new approach that improves the accuracy of a trained autoencoder’s results and answers the following question, Given a trained autoencoder, a test image, and using a real-parameter optimizer, can we generate better quality reconstructed image version than the one generated by the autoencoder?. Our proposed approach combines both the decoder part of a trained Resitricted Boltman Machine-based autoencoder with the Competitive Swarm Optimization algorithm. Experiments show that it is possible to reconstruct images using trained decoder from randomly initialized representations. Results also show that our approach reconstructed better quality images than the autoencoder in most of the test cases. Indicating that, we can use the approach for improving the performance of a pre-trained autoencoder if it does not give satisfactory results.

text: Using swarm optimization to enhance autoencoder’s images Autoencoders learn data representations through reconstruction. Robust training is the key factor affecting the quality of the learned representations and, consequently, the accuracy of the application that use them. Previous works suggested methods for deciding the optimal autoencoder configuration which allows for robust training. Nevertheless, improving the accuracy of a trained autoencoder has got limited, if no, attention. We propose a new approach that improves the accuracy of a trained autoencoder’s results and answers the following question, Given a trained autoencoder, a test image, and using a real-parameter optimizer, can we generate better quality reconstructed image version than the one generated by the autoencoder?. Our proposed approach combines both the decoder part of a trained Resitricted Boltman Machine-based autoencoder with the Competitive Swarm Optimization algorithm. Experiments show that it is possible to reconstruct images using trained decoder from randomly initialized representations. Results also show that our approach reconstructed better quality images than the autoencoder in most of the test cases. Indicating that, we can use the approach for improving the performance of a pre-trained autoencoder if it does not give satisfactory results.

year: 2017

title: The Waikato range imager

authors: Cree, Michael J.

subjects: range imaging

abstract: We are developing a high precision simultaneous full-field acquisition range imager. This device measures range with sub millimetre precision in range simultaneously over a full-field view of the scene. Laser diodes are used to illuminate the scene with amplitude modulation with a frequency of 10MHz up to 100 MHz. The received light is interrupted by a high speed shutter operating in a heterodyne configuration thus producing a low-frequency signal which is sampled with a digital camera. By detecting the phase of the signal at each pixel the range to the scene is determined. We show 3D reconstructions of some viewed objects to demonstrate the capabilities of the ranger.

text: The Waikato range imager We are developing a high precision simultaneous full-field acquisition range imager. This device measures range with sub millimetre precision in range simultaneously over a full-field view of the scene. Laser diodes are used to illuminate the scene with amplitude modulation with a frequency of 10MHz up to 100 MHz. The received light is interrupted by a high speed shutter operating in a heterodyne configuration thus producing a low-frequency signal which is sampled with a digital camera. By detecting the phase of the signal at each pixel the range to the scene is determined. We show 3D reconstructions of some viewed objects to demonstrate the capabilities of the ranger.

year: 2006

title: Te Mātaiaho as curricular justice?

authors: Shafer, Derek

abstract: Te Mātaiaho produces a version of curricular justice which aims to give effect to Te Tiriti and address long-standing inequities in education in Aotearoa New Zealand. We examine Te Mātaiaho’s vision for subject English and its construction of students’ identities as fundamental to a curriculum that advances both bicultural nationhood and just outcomes for all students. We draw on Raewyn Connell’s (2010) model of curricular justice, and Nancy Fraser's (2017) notion of progressive neoliberalism to explore the opportunities and implications for subject English within Te Mātaiaho's vision. How might these frameworks for justice help to critique the development of educational policy and support the implementation of Te Mātaiaho's vision for biculturalnationhood and just outcomes for all students.

text: Te Mātaiaho as curricular justice? Te Mātaiaho produces a version of curricular justice which aims to give effect to Te Tiriti and address long-standing inequities in education in Aotearoa New Zealand. We examine Te Mātaiaho’s vision for subject English and its construction of students’ identities as fundamental to a curriculum that advances both bicultural nationhood and just outcomes for all students. We draw on Raewyn Connell’s (2010) model of curricular justice, and Nancy Fraser's (2017) notion of progressive neoliberalism to explore the opportunities and implications for subject English within Te Mātaiaho's vision. How might these frameworks for justice help to critique the development of educational policy and support the implementation of Te Mātaiaho's vision for biculturalnationhood and just outcomes for all students.

year: 2023-11-22