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1. https://www.youtube.com/watch?v=WbaVvlgxbl4

2. Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

3. T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

4. CLIP: Connecting Text and Images: We’re introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision. CLIP can be applied to any visual classification benchmark by simply providing the names of the visual categories to be recognized, similar to the ‘zero-shot’ capabilities of GPT-2 and GPT-3

5. https://deepimagination.cc/eDiff-I/

6. https://openai.com/dall-e-2

7. Stable Diffusion Public Release

8. Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

9. https://parti.research.google/

10. Microsoft COCO: Common Objects in Context

11. http://visualgenome.org/

12. CDM: Cascaded Diffusion Models for High Fidelity Image Generation

13. Wikipedia Bibliography:

    1. Ensemble learning

    2. Fréchet inception distance