تقرير
Distance in Latent Space as Novelty Measure
| العنوان: | Distance in Latent Space as Novelty Measure |
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| المؤلفون: | Philipsen, Mark Philip, Moeslund, Thomas Baltzer |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Deep Learning performs well when training data densely covers the experience space. For complex problems this makes data collection prohibitively expensive. We propose to intelligently select samples when constructing data sets in order to best utilize the available labeling budget. The selection methodology is based on the presumption that two dissimilar samples are worth more than two similar samples in a data set. Similarity is measured based on the Euclidean distance between samples in the latent space produced by a DNN. By using a self-supervised method to construct the latent space, it is ensured that the space fits the data well and that any upfront labeling effort can be avoided. The result is more efficient, diverse, and balanced data set, which produce equal or superior results with fewer labeled examples. Comment: work in progress |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2003.14043 |
| رقم الأكسشن: | edsarx.2003.14043 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |