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Found 10 papers, 0 papers with code
DUEL: Duplicate Elimination on Active Memory for Self-Supervised Class-Imbalanced Learning
Recent machine learning algorithms have been developed using well-curated datasets, which often require substantial cost and resources.
L-SA: Learning Under-Explored Targets in Multi-Target Reinforcement Learning
Tasks that involve interaction with various targets are called multi-target tasks.
Learning Geometry-aware Representations by Sketching
Our method, coined Learning by Sketching (LBS), learns to convert an image into a set of colored strokes that explicitly incorporate the geometric information of the scene in a single inference step without requiring a sketch dataset.
DUEL: Adaptive Duplicate Elimination on Working Memory for Self-Supervised Learning
In Self-Supervised Learning (SSL), it is known that frequent occurrences of the collision in which target data and its negative samples share the same class can decrease performance.
Robust Imitation via Mirror Descent Inverse Reinforcement Learning
Recently, adversarial imitation learning has shown a scalable reward acquisition method for inverse reinforcement learning (IRL) problems.
Deep Quotient Manifold Modeling
One of the difficulties in modeling real-world data is their complex multi-manifold structure due to discrete features.
Unbiased learning with State-Conditioned Rewards in Adversarial Imitation Learning
The formulation draws a strong connection between adversarial learning and energy-based reinforcement learning; thus, the architecture is capable of recovering a reward function that induces a multi-modal policy.
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning
Active learning is widely used to reduce labeling effort and training time by repeatedly querying only the most beneficial samples from unlabeled data.
Manifold Learning and Alignment with Generative Adversarial Networks
We present a generative adversarial network (GAN) that conducts manifold learning and alignment (MLA): A task to learn the multi-manifold structure underlying data and to align those manifolds without any correspondence information.
Encoder-Powered Generative Adversarial Networks
We present an encoder-powered generative adversarial network (EncGAN) that is able to learn both the multi-manifold structure and the abstract features of data.
