International Business Machines Corporation

Xiao Tian Xu

Ze Ming Zhao

Li Cao

Peng Hui Jiang

Zhi Zhang

Yi Shan Jiang

Yu Song

Zhiwen Fu

Wenjing Liao

The present invention provides a computer-implemented method, a system, and a computer program product for path planning According to the computer-implemented method, a target discriminator is selected from a set of discriminators with different kernel sizes based on a target image obtained from an image capturing device. In this case, a confidence of the target image is determined using the target discriminator. The confidence indicates whether the target image contains a target object to be captured. Thereby, a movement indication for moving the image capturing device to capture the target object is determined based on the confidence of the target image.

Path planning

Embodiments of the present disclosure provide systems and methods for implementing self-bias detection based on performance and importance. A disclosed computer implemented method aggregates continuous input data through a K-means clustering algorithm to reduce the number of aggregated sub-group data pairs, enabling a reduced calculation time for computing bias and enhanced performance. The self-bias detection identifies a scale factor and a balance factor of aggregated sub-group data pairs, which indicate the importance of the detected bias.

Optimized bias self-detection based on performance and importance

Embodiments of the present disclosure relate to weight matrix prediction. In an embodiment, a computer-implemented method is disclosed. The method comprises sending a candidate weight matrix of a neural network to one of a plurality of computing nodes comprised in a computing system to perform a testing iteration. The method further comprises receiving a testing loss value from the one of the plurality of computing nodes based on the testing iteration. The method further comprises evaluating whether the testing loss value is applicable. The method further comprises determining that the candidate weight matrix is available to be employed in a new formal iteration in response to the testing loss value being applicable. In other embodiments, a system and a computer program product are disclosed.

Weight matrix prediction

A method for dividing, by a training system, a computational training work load of one or more neural network layers; pre-training the one or more neural network layers with a first class of image data sensitive to an original known dataset; generating a first weight file from the first layer of the neural network based on the first class of image data sensitive to the original known dataset; loading the one or more pre-trained neural network layers and the generated first weight file into at least one Internet of Things (IoT) device; stacking the one or more pre-trained neural network layers with the first layer of the neural network to form a new training system for an uploaded new dataset; adjusting the generated first weight file based on an input of one or more new classes of image data comprised in the uploaded new dataset to generate a new second weight file; inferencing an object class of new image data comprised on the uploaded new dataset using the generated new second weight file; and outputting the inferenced object class of the new image data.

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Changde, China

Xiao Tian Xu