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Found 4 papers, 2 papers with code
TcGAN: Semantic-Aware and Structure-Preserved GANs with Individual Vision Transformer for Fast Arbitrary One-Shot Image Generation
One-shot image generation (OSG) with generative adversarial networks that learn from the internal patches of a given image has attracted world wide attention.
Fuzzy Knowledge Distillation from High-Order TSK to Low-Order TSK
High-order Takagi-Sugeno-Kang (TSK) fuzzy classifiers possess powerful classification performance yet have fewer fuzzy rules, but always be impaired by its exponential growth training time and poorer interpretability owing to High-order polynomial used in consequent part of fuzzy rule, while Low-order TSK fuzzy classifiers run quickly with high interpretability, however they usually require more fuzzy rules and perform relatively not very well.
Sepformer-based Models: More Efficient Models for Long Sequence Time-Series Forecasting
These include: 1) complexity - Informer has a relatively high computational complexity and a high memory overhead; 2) nuance - there is limited ability to capture the subtle features in a data stream; 3) interpretability - the inference procedure of Informer is not explainable; 4) extensibility - accuracy is poor with extra-long multivariate time series.
A robust feature reinforcement framework for heterogeneous graphs neural networks
Heterogeneous graph embedding is a crucial step in HGNNs.
