Meet DIsoN, our 🧹💨 privacy-preserving OOD detector that compares test samples to training data without ever sharing the training data.
We make Out-of-Distribution detection decentralized!
📄Paper: arxiv.org/pdf/2506.09024
🧵👇
📍 Poster #1700, Exhibit Hall C–E
⏰ 4:30–7:30 PM (coming up soon!)
If you’re curious about decentralized OOD detection, come by and say hi! 👋
#AI #DL
📍 Poster #1700, Exhibit Hall C–E
⏰ 4:30–7:30 PM (coming up soon!)
If you’re curious about decentralized OOD detection, come by and say hi! 👋
#AI #DL
If you’re working on medical imaging, foundation models, multimodal learning, federated learning, or OOD detection, let’s meet! Happy to grab a coffee ☕️ or beer 🍺.
DM me to connect! 🌴
If you’re working on medical imaging, foundation models, multimodal learning, federated learning, or OOD detection, let’s meet! Happy to grab a coffee ☕️ or beer 🍺.
DM me to connect! 🌴
Meet DIsoN, our 🧹💨 privacy-preserving OOD detector that compares test samples to training data without ever sharing the training data.
We make Out-of-Distribution detection decentralized!
📄Paper: arxiv.org/pdf/2506.09024
🧵👇
Meet DIsoN, our 🧹💨 privacy-preserving OOD detector that compares test samples to training data without ever sharing the training data.
We make Out-of-Distribution detection decentralized!
📄Paper: arxiv.org/pdf/2506.09024
🧵👇
🔗 arXiv: arxiv.org/pdf/2406.11636
💻 GitHub: github.com/FelixWag/Fed...
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🔗 arXiv: arxiv.org/pdf/2406.11636
💻 GitHub: github.com/FelixWag/Fed...
🧵1/N