We introduce ALIA (Automated Language-guided Image Augmentation), a method which utilizes large vision and language models to automatically generate natural language descriptions of a dataset's domains and augment the training data via language-guided image editing.

Despite their demonstrated utility for NLP, multimodal counterfactual examples have been relatively unexplored due to the difficulty of creating paired image-text data with minimal counterfactual changes. To address this challenge, we introduce a scalable framework for automatic generation of counterfactual examples using text-to-image diffusion models.

By pre-training the model on domain-specific data, PubMED BERT is expected to have a better understanding of biomedical concepts, terminology, and language patterns compared to general domain models like BERT -base and BERT -large [ 95 ]. The main advantage of using PubMED BERT for biomedical text mining tasks is its domain-specific knowledge, which can lead to improved performance and more accurate results when fine-tuned on various downstream tasks, such as named entity recognition, relation extraction, document classification, and question answering. Since PubMED BERT is pre-trained on a large corpus of biomedical text, it is better suited to capturing the unique language patterns, complex terminology, and the relationships between entities in the biomedical domain.

Many WSAD methods attribute their improvements to novel network architectures or loss functions, based on the authors' understanding of

Data augmentation has been proven effective for training high-accuracy convolu-tional neural network classifiers by preventing overfitting.

Mixup refers to interpolation-based data augmentation, originally motivated as a way to go beyond empirical risk minimization (ERM). Its extensions mostly focus on the definition of interpolation and the space (input or embedding) where it takes place, while the augmentation process itself is less studied. In most methods, the number of generated examples is limited to the mini-batch size and the number of examples being interpolated is limited to two (pairs), in the input space. We make progress in this direction by introducing MultiMix, which generates an arbitrarily large number of interpolated examples beyond the mini-batch size, and interpolates the entire mini-batch in the embedding space.