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Exceeding the Limits of Visual-Linguistic Multi-Task Learning
By leveraging large amounts of product data collected across hundreds of live e-commerce websites, we construct 1000 unique classification tasks that share similarly-structured input data, comprised of both text and images.
E-Stitchup: Data Augmentation for Pre-Trained Embeddings
In this work, we propose data augmentation methods for embeddings from pre-trained deep learning models that take a weighted combination of a pair of input embeddings, as inspired by Mixup, and combine such augmentation with extra label softening.
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