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Found 5 papers, 4 papers with code
Auto-Meta: Automated Gradient Based Meta Learner Search
Fully automating machine learning pipelines is one of the key challenges of current artificial intelligence research, since practical machine learning often requires costly and time-consuming human-powered processes such as model design, algorithm development, and hyperparameter tuning.
Continual Learning with Deep Generative Replay
Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting.
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations.
Ranked #10 on
Facial Expression Translation
on CelebA
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
We present a highly accurate single-image super-resolution (SR) method.
Ranked #4 on
Image Super-Resolution
on WebFace - 8x upscaling
Deeply-Recursive Convolutional Network for Image Super-Resolution
We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN).
Ranked #20 on
Image Super-Resolution
on Urban100 - 2x upscaling
