So sánh phương pháp

	Mạng nơ-ron tích chập đa phương thức	Transfer Learning với Mạng Nơ-ron Tích chập
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2011	2010–2014
Người khởi xướng	Ngiam, J. et al. / multiple groups	Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al.
Loại	Multimodal deep learning model	Transfer learning applied to convolutional neural networks
Công trình gốc	Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal deep learning. In Proceedings of the 28th International Conference on Machine Learning (ICML), 689–696.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Tên gọi khác	MM-CNN, multimodal CNN, multi-input CNN, cross-modal convolutional network	TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN
Liên quan	5	4
Tóm tắt	A Multimodal Convolutional Neural Network (MM-CNN) processes and fuses two or more input modalities — such as images and text, or video and audio — through dedicated convolutional branches, learning a shared representation that captures complementary signals from each source. The fused representation drives a downstream task such as classification, regression, or retrieval.	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.