Linganisha mbinu

	Kielelezo cha Tabaka Nyingi cha Njia Nyingi	Multimodal Convolutional Neural Network
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2011 (multimodal extension); 1986 (MLP backpropagation)	2011
Mwanzilishi	Ngiam et al. / Rumelhart, Hinton & Williams (MLP foundations)	Ngiam, J. et al. / multiple groups
Aina	Feedforward neural network with multi-stream fusion	Multimodal deep learning model
Chanzo asilia	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 2011), pp. 689–696.	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.
Majina mbadala	MM-MLP, multimodal MLP, multi-input feedforward network, fusion multilayer perceptron	MM-CNN, multimodal CNN, multi-input CNN, cross-modal convolutional network
Zinazohusiana	5	5
Muhtasari	A Multimodal Multilayer Perceptron (MM-MLP) is a feedforward neural network that ingests features from two or more heterogeneous input modalities — such as structured tabular data, text embeddings, and image feature vectors — by encoding each stream separately and fusing them into a shared representation before passing it through fully connected layers to produce a classification or regression output.	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.