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Found 8 papers, 4 papers with code
Learning Efficient Planning-based Rewards for Imitation Learning
Our method learns useful planning computations with a meaningful reward function that focuses on the resulting region of an agent executing an action.
A Simple Sparse Denoising Layer for Robust Deep Learning
In this work, we take an initial step to designing a simple robust layer as a lightweight plug-in for vanilla deep models.
Intrinsic Reward Driven Imitation Learning via Generative Model
Thus, our module provides the imitation agent both the intrinsic intention of the demonstrator and a better exploration ability, which is critical for the agent to outperform the demonstrator.
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework
The novelty of the proposed framework focuses on incorporating deep adversarial learning with statistical learning and exploiting learning based data augmentation.
How does Disagreement Help Generalization against Label Corruption?
Learning with noisy labels is one of the hottest problems in weakly-supervised learning.
Ranked #13 on
Learning with noisy labels
on CIFAR-100N
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Given data with noisy labels, over-parameterized deep networks can gradually memorize the data, and fit everything in the end.
Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels
To handle these issues, by using the memorization effects of deep neural networks, we may train deep neural networks on the whole dataset only the first few iterations.
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training.
Ranked #8 on
Learning with noisy labels
on CIFAR-10N-Random3
