পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| সেল্ফ-সুপারভাইজড র্যান্ডম ফরেস্ট× | স্ব-পর্যবেক্ষণাধীন শিখন× | |
|---|---|---|
| ক্ষেত্র | যন্ত্র শিখন | যন্ত্র শিখন |
| পরিবার | Machine learning | Machine learning |
| উদ্ভবের বছর≠ | 2012–2022 | 2018–2020 |
| প্রবর্তক≠ | Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage) | LeCun, Y. and community (formalized ~2018–2020) |
| ধরন≠ | Semi-supervised ensemble (self-supervised pretext task + RF) | Representation learning paradigm |
| মৌলিক উৎস≠ | Lefortier, D., Chitta, K., & Agrawal, P. (2022). Self-supervised random forests. arXiv:2204.01430. link ↗ | LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/ link ↗ |
| অপর নাম | SSL-RF, self-supervised RF, self-supervised ensemble forest, unsupervised random forest with self-labeling | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning |
| সম্পর্কিত≠ | 6 | 3 |
| সারসংক্ষেপ≠ | Self-supervised Random Forest (SSL-RF) extends the classic random forest to settings where labeled examples are scarce. The forest is first trained using automatically generated pseudo-labels derived from a self-supervised pretext task — such as predicting data transformations or masked features — and then refined on whatever true labels are available, marrying the label-efficiency of self-supervised learning with the robustness of ensemble trees. | Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples. |
| ScholarGateডেটাসেট ↗ |
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