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Found 5 papers, 2 papers with code
Noisy-label Learning with Sample Selection based on Noise Rate Estimate
Even though the estimated noise rate from the training set appears to be a natural signal to be used in the definition of this curriculum, previous approaches generally rely on arbitrary thresholds or pre-defined selection functions to the best of our knowledge.
PASS: Peer-Agreement based Sample Selection for training with Noisy Labels
To address this limitation, we propose a novel peer-agreement based sample selection (PASS).
Instance-Dependent Noisy Label Learning via Graphical Modelling
Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because models can easily overfit them.
Ranked #1 on
Learning with noisy labels
on CIFAR-100
PeR-ViS: Person Retrieval in Video Surveillance using Semantic Description
Instead of using an image query, in this paper, we study the problem of person retrieval in video surveillance with a semantic description.
Defensive Escort Teams via Multi-Agent Deep Reinforcement Learning
When compared to a state-of-art algorithm for obstacle avoidance, our solution with a single escort increases navigation success up to 31%.
