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Found 3 papers, 2 papers with code
Responsible Active Learning via Human-in-the-loop Peer Study
To further enhance the active learner via large-scale unlabelled data, we introduce multiple peer students into the active learner which is trained by a novel learning paradigm, including the In-Class Peer Study on labelled data and the Out-of-Class Peer Study on unlabelled data.
Knowledge-Aware Federated Active Learning with Non-IID Data
In this paper, we propose a federated active learning paradigm to efficiently learn a global model with limited annotation budget while protecting data privacy in a decentralized learning way.
Symbiotic Adversarial Learning for Attribute-based Person Search
The current state-of-the-art methods either focus on learning better cross-modal embeddings by mining only seen data, or they explicitly use generative adversarial networks (GANs) to synthesize unseen features.
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