Transformer Models: Not Just for Text Anymore

Transformer models have made significant impacts beyond natural language processing (NLP), with applications in computer vision, tabular data modeling, recommender systems, and reinforcement learning. Vision Transformers (ViT) and DETR have emerged as strong competitors in image classification, object detection, and segmentation tasks. Transformers for tabular data include models like TabTransformer and SAINT that employ self-attention mechanisms to capture relationships between various vectors. In recommender systems, BERT4Rec uses the BERT architecture to build a powerful sequential recommendation system by capturing both short-term and long-term patterns. Transformers have also been adapted for reinforcement learning tasks, with models like Gated Transformer-XL and TrMRL showcasing improved performance and generalization capabilities. However, it's important to remember that self-attention is not the only solution for all machine learning problems, as evidenced by the need for hybrid architectures in some cases.