手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| マルチモーダル畳み込みニューラルネットワーク× | 畳み込みニューラルネットワークを用いた転移学習× | |
|---|---|---|
| 分野 | 深層学習 | 深層学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2011 | 2010–2014 |
| 提唱者≠ | 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. |
| 種類≠ | Multimodal deep learning model | Transfer learning applied to convolutional neural networks |
| 原典≠ | 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. link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| 別名 | MM-CNN, multimodal CNN, multi-input CNN, cross-modal convolutional network | TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN |
| 関連≠ | 5 | 4 |
| 概要≠ | 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. |
| ScholarGateデータセット ↗ |
|
|