Privacy-aware deep learning for gender detection
With the recent advancements made in deep learning, it is clear that deep learning has become the most promising approach in artificial intelligence to tackle complex problems. Deep learning has shown its prowess in being able to learn large amounts of features due to its substantial learning capaci...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
Nanyang Technological University
2021
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/150349 |
| الوسوم: |
إضافة وسم
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| المؤسسة: | Nanyang Technological University |
| اللغة: | English |
| الملخص: | With the recent advancements made in deep learning, it is clear that deep learning has become the most promising approach in artificial intelligence to tackle complex problems. Deep learning has shown its prowess in being able to learn large amounts of features due to its substantial learning capacity.
This report is a documentation of the progress of a Final Year Project. The aim is to Incorporate Generative Adversarial Privacy to achieve Gender neutrality of a face image coupled with A skin disease identifier created using YOLO. Being able to preserve the patient's identity while identifying a skin disease. So as to encourage patients to use medical application systems with heightened privacy and also to give a second opinion on common skin diseases. |
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