Multimodal Deep learning

tags:
  - multimodal
  - deep_learning
  - classification
  - models
aliases:
  - Multi-modal Learning

Multimodal deep learning is the discipline of machine learning where the input consists of different modalities, ie. data with a different nature of interpretation such as sound and image .

Multi-modal learning with deep learning is a subfield of machine learning that focuses on training models to process and learn from multiple data sources (modalities). These modalities can be diverse and include:

Images: Capturing visual information
Text: Providing textual descriptions, labels, or captions
Audio: Containing sounds or speech
Sensor data: Offering measurements like temperature, pressure, or acceleration
LiDAR data: Providing 3D point cloud information

The key objective is to leverage the complementary information present in these diverse data sources to create a richer understanding and improve performance on various tasks, such as:

Image classification: Combining image data with textual descriptions to improve the accuracy of identifying objects in images.
Machine translation: Utilizing both audio recordings of spoken language and corresponding text transcripts to enhance translation quality.

Late Fusion with Pre-training in Multi-modal Learning:

In a late fusion approach with multi-modal learning, separate sub-models are pre-trained on individual data modalities before being combined for the final task. This pre-training offers several benefits:

Leverage existing knowledge: Each sub-model can leverage existing knowledge from pre-trained models on similar data, improving its learning efficiency and performance. For example, a sub-model for image data may be pre-trained on a large image dataset like ImageNet, while a sub-model for text data might be pre-trained on a massive text corpus like Wikipedia.
Reduce training complexity: Pre-training each sub-model on specific data types simplifies the overall training process and reduces the computational burden of training a single model from scratch on all modalities combined.
Improve feature representation: By pre-training, each sub-model can learn effective feature representations specific to its corresponding data modality. These learned features can then be effectively combined during the late fusion stage.

There are various techniques for pre-training sub-models, depending on the specific data modalities and desired task:

Transfer Learning: This involves utilizing pre-trained models on similar tasks and data but different modalities. For example, a pre-trained image classification model (e.g., VGG-16) can be utilized as a feature extractor for image data in a multi-modal classification task.
Self-supervised Learning: This approach utilizes the data itself to create pseudo-labels or tasks for pre-training. For example, in image data, pre-training can involve predicting image rotations, identifying missing image patches, or coloring grayscale images.
Multi-task Learning: This method involves training a single model on multiple related tasks simultaneously. While not strictly pre-training, this technique can improve the model's ability to learn transferable features across different modalities.

Overall, pre-training sub-models in a late fusion approach can significantly benefit multi-modal learning with deep learning by leveraging existing knowledge, reducing training complexity, and improving feature representation for the final task.

Multi-modal Learning with Deep Learning

Late Fusion with Pre-training in Multi-modal Learning: