Title | Forward-Forward Algorithms for Self-Supervised Training of Deep Networks |
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Description |
Forward Forward algorithms [1,2] offer an alternative to traditional backpropagation training of deep networks. Unlike backpropagation, where spreading the computation over multiple devices requires synchronisation and wait-times [3], in forward-forward optimization training targets are not propagated through the network, eliminating the need for wait-times during training.
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Qualification |
If you are interested, please write a meaningful email that addresses your previous experience, interests, and strengths. |
Proposal |
In this thesis, we explore the connections with self-supervised learning using contrastive losses [4].
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Thesistype | Bachelorthesis |
Second Tutor | Pfahler, Lukas |
Professor | Pfahler, Lukas |
Status | Offen |
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