Attribute
1410 papers with code • 1 benchmarks • 1 datasets
Libraries
Use these libraries to find Attribute models and implementationsMost implemented papers
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection.
Analyzing and Improving the Image Quality of StyleGAN
Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model.
CIDEr: Consensus-based Image Description Evaluation
We propose a novel paradigm for evaluating image descriptions that uses human consensus.
Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
This work summarizes the results of the largest skin image analysis challenge in the world, hosted by the International Skin Imaging Collaboration (ISIC), a global partnership that has organized the world's largest public repository of dermoscopic images of skin.
Deep Feature Consistent Variational Autoencoder
We present a novel method for constructing Variational Autoencoder (VAE).
StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN.
Sampling Generative Networks
We introduce several techniques for sampling and visualizing the latent spaces of generative models.
Closed-Form Factorization of Latent Semantics in GANs
A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images.
AttGAN: Facial Attribute Editing by Only Changing What You Want
Based on the encoder-decoder architecture, facial attribute editing is achieved by decoding the latent representation of the given face conditioned on the desired attributes.