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Found 8 papers, 5 papers with code
Aligning and Projecting Images to Class-conditional Generative Networks
We present a method for projecting an input image into the space of a class-conditional generative neural network.
Learning to Ground Multi-Agent Communication with Autoencoders
Communication requires having a common language, a lingua franca, between agents.
The Low-Rank Simplicity Bias in Deep Networks
We show empirically that our claim holds true on finite width linear and non-linear models on practical learning paradigms and show that on natural data, these are often the solutions that generalize well.
Transforming and Projecting Images into Class-conditional Generative Networks
We present a method for projecting an input image into the space of a class-conditional generative neural network.
Feedback Adversarial Learning: Spatial Feedback for Improving Generative Adversarial Networks
We propose feedback adversarial learning (FAL) framework that can improve existing generative adversarial networks by leveraging spatial feedback from the discriminator.
Multi-view to Novel view: Synthesizing Novel Views with Self-Learned Confidence
We address the task of multi-view novel view synthesis, where we are interested in synthesizing a target image with an arbitrary camera pose from given source images.
Ranked #1 on
Novel View Synthesis
on KITTI Novel View Synthesis
Fighting Fake News: Image Splice Detection via Learned Self-Consistency
In this paper, we propose a learning algorithm for detecting visual image manipulations that is trained only using a large dataset of real photographs.
What makes ImageNet good for transfer learning?
Which is better: more classes or more examples per class?
