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Found 2 papers, 1 papers with code
ATCON: Attention Consistency for Vision Models
We propose an unsupervised fine-tuning method that optimizes the consistency of attention maps and show that it improves both classification performance and the quality of attention maps.
Efficient Exploration via First-Person Behavior Cloning Assisted Rapidly-Exploring Random Trees
In each test, CA-RRT reached more states on average in the same number of iterations as weighted-RRT.
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