OpenAI CLIP – Connecting Text and Images | Paper Explained

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In this video, I cover the CLIP paper – Learning Transferable Visual Models from Natural Language Supervision.

You’ll learn about: How the contrastive learning behind CLIP works All the nitty-gritty details behind the paper

▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Bitter lessons by Sutton: http://www.incompleteideas.net/IncIdeas/BitterLesson.html CLIP paper: https://cdn.openai.com/papers/Learning_Transferable_Visual_Models_From_Natural_Language.pdf▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬

Timetable:

00:00 OpenAI’s CLIP02:10 Detailed explanation of the method06:00 Comparision with SimCLR12:55 How does the zero-shot part work20:45 WIT dataset21:30 Why this method, hint efficiency28:35 Zero-shot – generalizing to new tasks31:30 Prompt programming and ensembling34:00 Zero-shot perf36:20 Few-shot comparison with best baselines38:20 How good the zero-shot classifier is?40:45 Compute error correlation41:20 Quality of CLIP’s embedding space43:05 Robustness to distribution shift49:10 Limitations (MNIST failure)50:30 A short recap

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