Deep Active Learning for Surface Defect Detection
نویسندگان
چکیده
منابع مشابه
Melanoma detection with a deep learning model
Background: Skin cancer is one of the most common forms of cancer in the world and melanoma is the deadliest type of skin cancer. Both melanoma and melanocytic nevi begin in melanocytes (cells that produce melanin). However, melanocytic nevi are benign whereas melanoma is malignant. This work proposes a deep learning model for classification of these two lesions. Methods: In this analytic s...
متن کاملConcept drift detection in business process logs using deep learning
Process mining provides a bridge between process modeling and analysis on the one hand and data mining on the other hand. Process mining aims at discovering, monitoring, and improving real processes by extracting knowledge from event logs. However, as most business processes change over time (e.g. the effects of new legislation, seasonal effects and etc.), traditional process mining techniques ...
متن کاملDeep Learning for Cloud Detection
The SPOT 6-7 satellite ground segment includes a systematic and automatic cloud detection step in order to feed a catalogue with a binary cloud mask and an appropriate confidence measure. However, current approaches for cloud detection, that are mostly based on machine learning and hand crafted features, have shown lack of robustness. In other tasks such as image recognition, deep learning meth...
متن کاملUsing machine learning for defect detection
In this paper we present an approach to defect detection in images based on machine learning algorithms. A qualitative model of defect has been devised based on human experience. A set of vision primitives measuring defect features has been deened. Objects in images candidate to be classiied as defects are submitted to automatic classiication, which is achieved with learning by examples algorit...
متن کاملDeep Active Learning for Dialogue Generation
We propose an online, end-to-end, neural generative conversational model for opendomain dialogue. It is trained using a unique combination of offline two-phase supervised learning and online human-inthe-loop active learning. While most existing research proposes offline supervision or hand-crafted reward functions for online reinforcement, we devise a novel interactive learning mechanism based ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Sensors
سال: 2020
ISSN: 1424-8220
DOI: 10.3390/s20061650