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Found 4 papers, 2 papers with code
Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution
Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions.
Network Space Search for Pareto-Efficient Spaces
We propose an NSS method to directly search for efficient-aware network spaces automatically, reducing the manual effort and immense cost in discovering satisfactory ones.
Exploration via Flow-Based Intrinsic Rewards
Exploration bonuses derived from the novelty of observations in an environment have become a popular approach to motivate exploration for reinforcement learning (RL) agents in the past few years.
Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow
We propose to employ optical flow estimation errors to examine the novelty of new observations, such that agents are able to memorize and understand the visited states in a more comprehensive fashion.
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