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Found 5 papers, 2 papers with code
Text2Model: Text-based Model Induction for Zero-shot Image Classification
Our approach generates non-linear classifiers and can handle rich textual descriptions.
Example-based Hypernetworks for Out-of-Distribution Generalization
Our innovative framework employs example-based Hypernetwork adaptation: a T5 encoder-decoder initially generates a unique signature from an input example, embedding it within the source domains' semantic space.
On-Demand Unlabeled Personalized Federated Learning
In both FL and PFL, all clients participate in the training process and their labeled data are used for training.
Inference-Time Personalized Federated Learning
That client representation is fed to a hypernetwork that generates a personalized model for that client.
Teacher-Student Consistency For Multi-Source Domain Adaptation
Then, we train a student network using the pseudo labels and regularized the teacher to fit the student predictions.
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