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Found 10 papers, 7 papers with code
Edit One for All: Interactive Batch Image Editing
With increased human control, it is now possible to edit an image in a plethora of ways; from specifying in text what we want to change, to straight up dragging the contents of the image in an interactive point-based manner.
Visual Instruction Inversion: Image Editing via Visual Prompting
Given pairs of example that represent the "before" and "after" images of an edit, our goal is to learn a text-based editing direction that can be used to perform the same edit on new images.
Towards Universal Fake Image Detectors that Generalize Across Generative Models
In this work, we first show that the existing paradigm, which consists of training a deep network for real-vs-fake classification, fails to detect fake images from newer breeds of generative models when trained to detect GAN fake images.
Few-shot Image Generation via Cross-domain Correspondence
Training generative models, such as GANs, on a target domain containing limited examples (e. g., 10) can easily result in overfitting.
Ranked #3 on
10-shot image generation
on Babies
Generating Furry Cars: Disentangling Object Shape & Appearance across Multiple Domains
We consider the novel task of learning disentangled representations of object shape and appearance across multiple domains (e. g., dogs and cars).
Generating Furry Cars: Disentangling Object Shape and Appearance across Multiple Domains
We consider the novel task of learning disentangled representations of object shape and appearance across multiple domains (e. g., dogs and cars).
MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, for mix-and-match image generation.
Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data
We propose a novel unsupervised generative model that learns to disentangle object identity from other low-level aspects in class-imbalanced data.
FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery
We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained object categories.
Ranked #1 on
Image Clustering
on Stanford Cars
NAG: Network for Adversary Generation
Our trained generator network attempts to capture the distribution of adversarial perturbations for a given classifier and readily generates a wide variety of such perturbations.
