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Found 4 papers, 1 papers with code
Offline RL With Resource Constrained Online Deployment
We address this performance gap with a policy transfer algorithm which first trains a teacher agent using the offline dataset where features are fully available, and then transfers this knowledge to a student agent that only uses the resource-constrained features.
Offline Reinforcement Learning with Resource Constrained Online Deployment
We address this performance gap with a policy transfer algorithm which first trains a teacher agent using the offline dataset where features are fully available, and then transfers this knowledge to a student agent that only uses the resource-constrained features.
Representation Learning for Clustering via Building Consensus
Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techniques) must be close in the representation space (exemplar consistency), and/or similar images must have similar cluster assignments (population consistency).
Ranked #3 on
Image Clustering
on ImageNet-10
Consensus Clustering With Unsupervised Representation Learning
Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techniques) must either be closer in the representation space, or have a similar cluster assignment.
