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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

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✅ 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
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⌚️ Timetable:

00:00 OpenAI's CLIP
02:10 Detailed explanation of the method
06:00 Comparision with SimCLR
12:55 How does the zero-shot part work
20:45 WIT dataset
21:30 Why this method, hint efficiency
28:35 Zero-shot - generalizing to new tasks
31:30 Prompt programming and ensembling
34:00 Zero-shot perf
36:20 Few-shot comparison with best baselines
38:20 How good the zero-shot classifier is?
40:45 Compute error correlation
41:20 Quality of CLIP's embedding space
43:05 Robustness to distribution shift
49:10 Limitations (MNIST failure)
50:30 A short recap

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💡 The AI Epiphany is a channel dedicated to simplifying the field of AI using creative visualizations and in general, a stronger focus on geometrical and visual intuition, rather than the algebraic and numerical "intuition".

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#clip #openai #nlpsupervision