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Object-Centric Open-Vocabulary Image-Retrieval with Aggregated Features
Our approach aggregates dense embeddings extracted from CLIP into a compact representation, essentially combining the scalability of image retrieval pipelines with the object identification capabilities of dense detection methods.
Can Stochastic Gradient Langevin Dynamics Provide Differential Privacy for Deep Learning?
Bayesian learning via Stochastic Gradient Langevin Dynamics (SGLD) has been suggested for differentially private learning.
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