This AI Paper Proposes FACTORCL: A New Multimodal Representation Learning Method to Go Beyond Multi-View Redundancy

One of the main paradigms in machine learning is learning representations from several modalities. Pre-training broad pictures on unlabeled multimodal data and then fine-tuning ask-specific labels is a common learning strategy today. The present multimodal pretraining techniques are mostly derived from earlier research in multi-view learning, which capitalizes on a crucial premise of multi-view redundancy: […]

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