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- W4200205728 abstract "Recently, in the field of computer animation, a method for generating motion using deep learning has been studied away from conventional finite-state machines or graph-based methods. The expressiveness of the network required for learning motions is more influenced by the diversity of motion contained in it than by the simple length of motion to be learned. This study aims to find an efficient network structure when the types of motions to be learned are diverse. In this paper, we train and compare three types of networks: basic fully-connected structure, mixture of experts structure that uses multiple fully-connected layers in parallel, recurrent neural network which is widely used to deal with seq2seq, and transformer structure used for sequence-type data processing in the natural language processing field." @default.
- W4200205728 created "2021-12-31" @default.
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- W4200205728 date "2021-11-01" @default.
- W4200205728 modified "2023-09-25" @default.
- W4200205728 title "A Comparison of Deep Neural Network Structures for Learning Various Motions" @default.
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- W4200205728 doi "https://doi.org/10.15701/kcgs.2021.27.5.73" @default.
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