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Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool
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Sketches are abstract representations of visual perception and visuospatial construction. In this work, we proposed a new framework, Generative Adversarial Networks with Conditional Neural Movement Primitives (GAN-CNMP), that incorporates a novel adversarial loss on CNMP to increase sketch smoothness and consistency. Through the experiments, we show that our model can be trained with few unlabeled samples, can construct distributions automatically in the latent space, and produces better results than the base model in terms of shape consistency and smoothness.
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Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning
A hybrid meta-RL method uses offline-trained conditional neural processes to generate extra rollouts, enabling reward-free adaptation to an unseen task from a single real rollout.
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