Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents.
We propose a "transposed" version of self-attention that operates across feature channels rather than tokens, where the interactions are based on the cross-covariance matrix between keys and queries.
Ranked #3 on Self-Supervised Image Classification on ImageNet
Recently, research efforts have been concentrated on revealing how pre-trained model makes a difference in neural network performance.
This adversarial loss guarantees the map is diverse -- a very wide range of anime can be produced from a single content code.
Ranked #1 on Image-to-Image Translation on selfie2anime
Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model.
Ranked #1 on Graph Regression on PCQM4M-LSC
While several recent works investigate how to disentangle underlying factors of variation in the data, most of them operate in 2D and hence ignore that our world is three-dimensional.
The proposed GAN prior embedded network (GPEN) is easy-to-implement, and it can generate visually photo-realistic results.
Ranked #1 on Blind Face Restoration on CelebA-HQ