training transitive
Training Transitive and Commutative Multimodal Transformers with LoReTTa
Training multimodal foundation models is challenging due to the limited availability of multimodal datasets. While many public datasets pair images with text, few combine images with audio or text with audio. Even rarer are datasets that align all three modalities at once. Critical domains such as healthcare, infrastructure, or transportation are particularly affected by missing modalities. This makes it difficult to integrate all modalities into a large pre-trained neural network that can be used out-of-the-box or fine-tuned for different downstream tasks.
Appendix: Training Transitive and Commutative Multimodal Transformers with LoReTTa Manuel Tran
In our SVL-MNIST experiments, we freeze the backbone and train a linear classifier on top. The initial learning rate is 0.1, but it We do not use weight decay. It is particularly effective for problems with many features. We divide the SVL-MNIST dataset into training, validation, and test sets (Figure A1). The first dataset (I, T) consists of 12,000 paired samples from MNIST and WineReviews.
Training Transitive and Commutative Multimodal Transformers with LoReTTa
Training multimodal foundation models is challenging due to the limited availability of multimodal datasets. While many public datasets pair images with text, few combine images with audio or text with audio. Even rarer are datasets that align all three modalities at once. Critical domains such as healthcare, infrastructure, or transportation are particularly affected by missing modalities. This makes it difficult to integrate all modalities into a large pre-trained neural network that can be used out-of-the-box or fine-tuned for different downstream tasks.
Training Transitive and Commutative Multimodal Transformers with LoReTTa
Training multimodal foundation models is challenging due to the limited availability of multimodal datasets. While many public datasets pair images with text, few combine images with audio or text with audio. Even rarer are datasets that align all three modalities at once. Critical domains such as healthcare, infrastructure, or transportation are particularly affected by missing modalities. This makes it difficult to integrate all modalities into a large pre-trained neural network that can be used out-of-the-box or fine-tuned for different downstream tasks.