পদ্ধতির তুলনা করুন

	Semi-supervised Convolutional Neural Network	কনভোল্যুশনাল নিউরাল নেটওয়ার্ক (CNN) ব্যবহার করে ট্রান্সফার লার্নিং
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2013–2017	2010–2014
প্রবর্তক	Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others)	Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al.
ধরন	Semi-supervised deep learning	Transfer learning applied to convolutional neural networks
মৌলিক উৎস	Lee, D.-H. (2013). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. ICML Workshop on Challenges in Representation Learning.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
অপর নাম	SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN	TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN
সম্পর্কিত	5	4
সারসংক্ষেপ	A Semi-supervised CNN trains a convolutional network on a small labeled image set and a larger pool of unlabeled images simultaneously, using techniques such as pseudo-labeling and consistency regularization to extract supervisory signal from unlabeled data. This strategy closes much of the performance gap caused by scarce annotations without requiring additional human labeling effort.	Transfer Learning with CNN reuses a convolutional neural network that has already been trained on a large dataset — most commonly ImageNet — and adapts its learned feature detectors to a new, often smaller target dataset. This lets researchers achieve strong image-recognition performance without the massive compute and data resources required to train a CNN from scratch.