## Diagram: Knowledge Distillation Taxonomy ### Overview The image is a hierarchical diagram illustrating a taxonomy of knowledge distillation techniques for large transformer-based models. The diagram branches from a central topic into two main categories: Computer Vision and Natural Language Processing. Each of these categories is further divided into subcategories based on the distillation method used: Logits-based, Hint-based, and Others. Natural Language Processing has an additional category: API-based. Each subcategory lists specific models or techniques. ### Components/Axes * **Main Title:** Knowledge Distillation for Large Transformer-Based Models (Blue box at the top) * **First Level Categories:** * Computer Vision (Green box, left side) * Natural Language Processing (Red box, right side) * **Second Level Categories (Computer Vision):** * Logits-based (Purple box, left) * Description: Output logits * Hint-based (Yellow box, center-left) * Description: Intermediate features * Others (Gray box, right-left) * Description: Other contexts * **Second Level Categories (Natural Language Processing):** * Logits-based (Purple box, left) * Description: Output logits * Hint-based (Yellow box, center-left) * Description: Intermediate features * API-based (Orange box, center-right) * Description: Generated contexts * Others (Gray box, right) * Description: Parameters ### Detailed Analysis or ### Content Details **Computer Vision:** * **Logits-based:** * DeiT * TinyViT * **Hint-based:** * ViTKD * ManifoldKD * **Others:** * GPT4Image * BLIP **Natural Language Processing:** * **Logits-based:** * DistilBERT * MINILLM * **Hint-based:** * MobileBERT * TinyBERT * **API-based:** * PaD * Lion * **Others:** * Bert-of-theseus * ProKT ### Key Observations * The diagram provides a structured overview of knowledge distillation methods. * Computer Vision and Natural Language Processing are the two main application areas. * Logits-based and Hint-based approaches are common to both domains. * Natural Language Processing has an additional category, API-based, which is not present in Computer Vision. * The "Others" category exists in both domains, suggesting that there are knowledge distillation techniques that do not fit neatly into the Logits-based, Hint-based, or API-based categories. ### Interpretation The diagram illustrates the landscape of knowledge distillation techniques applied to large transformer-based models, specifically in the fields of Computer Vision and Natural Language Processing. The categorization highlights the different types of information used to transfer knowledge from a larger, more complex model (teacher) to a smaller, more efficient model (student). Logits-based methods focus on matching the output probabilities of the teacher, Hint-based methods use intermediate feature representations, and API-based methods leverage generated contexts. The "Others" category suggests the existence of less conventional or hybrid approaches. The diagram serves as a useful reference for researchers and practitioners in the field, providing a clear overview of the available techniques and their applications.