The article discusses the TTM framework's two-stage workflow consisting of pre-training and fine-tuning for an efficient AI model. The pre-training predominantly occurs in the TTM backbone, with challenges related to diverse, multi-resolution data. The authors explore options for handling this diversity, proposing enhancements such as data augmentation through downsampling to create datasets at lower resolutions, thus maximizing training data. Additionally, the technique of resolution prefix tuning is introduced to learn effective embeddings that adapt to different resolution patterns, ultimately improving performance and accuracy of the model across multiple seasons.
The proposed TTM framework utilizes a dual-stage process involving pre-training on diverse, multi-resolution datasets followed by fine-tuning, significantly enhancing model efficiency and accuracy.
To facilitate effective multi-resolution pre-training, a downsampling technique is introduced, allowing the generation of lower resolution datasets from higher resolution ones, thus augmenting training data.
Resolution prefix tuning plays a crucial role in the TTM workflow by learning a new patch embedding that recognizes and utilizes various resolution data patterns.
This innovative approach of collective pre-training across multiple resolutions not only addresses data scarcity but also stabilizes learning diverse seasonal patterns within the training protocol.
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