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
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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:
00:0…
Watch on YouTube ↗
(saves to browser)
Chapters (16)
OpenAI's CLIP
2:10
Detailed explanation of the method
6:00
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
Playlist
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