I am trying to do domain adaptation from synthetic to real images. The task is anomaly detection,
Usually the problem for domain adaptation is that there are a lot of target images without labels. In my case I only have a few target images with labels. Therefore, the common technique of creating pseudo labels for the target domain is not useful.
My current idea is to do some kind of style transfer from the synthetic images to the real images. (CyCADA, Contrastive unpaired image to image)
Do you have other ideas of domain adaptation where I have a lot of source images but only a few target images (but with ground truth)?
I would be glad to be pointed in the right direction.
A dataset of ~22,500 labeled documents across four different domains. You can find it here:
Consider two domains, cartoon and real world. I want my final network to take as input a rainy image and output the corresponding clear image. I have paired rainy-clear dataset in the cartoon domain. And I have unpaired rainy-clear images in the real domain. My network is doing well in the cartoon domain. How do I make it generalize well to the real domain. Could you direct me to research papers that do something similar?
Sketch Your GAN domain adaptation
Want to quickly train an entire GAN that generates realistic images from just two quick sketches done by hand? Heng-Yu Wang and team got you covered! They propose a new method to fine-tune a GAN to a small set of user-provided sketches that determine the shapes and poses of the objects on the synthesized images. They use domain adversarial loss and different regularization methods to preserve the original model's diversity and image quality.
The authors motivate the necessity of their approach mainly with the fact that training conditional GANs from scratch is simply a lot of work: you need powerful GPUs, annotated data, careful alignment, and pre-processing. In order for an end-user to generate images of a cats in a specific pose a very large number of such images is required, however with the proposed approach only a couple of sketches and a pretrained GAN is needed to create a new GAN that synthesizes images resembling the shape and orientation of sketches, and retains the diversity and quality of the original model. The resulting models can be used for random sampling, latent space interpolation and photo editing.
Read the full paper digest or the blog post (reading time ~5 minutes) to learn about Cross-Domain Adversarial Learning, how Image Space Regularization helps improve the results, and what optimization targets are used in Sketch Your Own GAN.
Meanwhile, check out the paper digest poster by Casual GAN Papers!
[Full Explanation/ Blog Post] [Arxiv] [Code]
More recent popular computer vision paper breakdowns:
>[3D-Inpainting]
>
>[Real-ESRGAN]
>
>[SupCon]
The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to solve such a problem. Recent works show that self-training is a powerful approach to UDA. However, existing methods have difficulty in balancing scalability and performance. In this paper, we propose an instance adaptive self-training framework for UDA on the task of semantic segmentation. To effectively improve the quality of pseudo-labels, we develop a novel pseudo-label generation strategy with an instance adaptive selector. Besides, we propose the region-guided regularization to smooth the pseudo-label region and sharpen the non-pseudo-label region. Our method is so concise and efficient that it is easy to be generalized to other unsupervised domain adaptation methods. Experiments on 'GTA5 to Cityscapes' and 'SYNTHIA to Cityscapes' demonstrate the superior performance of our approach compared with the state-of-the-art methods.
Just look at these crazy prompts!
How insane does it sound to describe a GAN with text (e.g. Human -> Werewolf) and get a SOTA generator that synthesizes images corresponding to the provided text query in any domain?! Rinon Gal and colleagues leverage the semantic power of CLIP's text-image latent space to shift a pretrained generator to a new domain. All it takes is a natural text prompts and a few minutes of training. The domains that StyleGAN-NADA covers are outright bizzare (and creepily specific) - Fernando Botero Painting, Dog → Nicolas Cage (WTF 😂), and more.
Usually it is hard (or outright impossible) to obtain a large number of images from a specific domain required to train a GAN. One can leverage the information learned by Vision-Language models such as CLIP, yet applying these models to manipulate pretrained generators to synthesize out-of-domain images is far from trivial. The authors propose to use dual generators and an adaptive layer selection procedure to increase training stability. Unlike prior works StyleGAN-NADA works in zero-shot manner and automatically selects a subset of layers to update at each iteration.
Read the full paper digest or the blog post (reading time ~5 minutes) to learn about Cross-Domain Adversarial Learning, how Image Space Regularization helps improve the results, and what optimization targets are used in Sketch Your Own GAN.
Meanwhile, check out the paper digest poster by Casual GAN Papers!
[Full Explanation / Blog Post] [Arxiv] [Code]
More recent popular computer vision paper breakdowns:
>[3D-Inpainting]
>
>[Real-ESRGAN]
>
>[[Sketch Your Own GAN](https://t.me/casual_gan/81
Interested in Universal Domain Adaptation? Join the VisDA 2021 Neurips competition!
This challenge will test how well models can (1) adapt to several distribution shifts and (2) detect unknown unknowns.
Top 3 teams will win $$$$ (2$k 1st/ $500 2nd and 3rd) in the form of VISA gift cards.
More details (and registration) at the website and below
-----------------------------------------------------------------------------
Progress in machine learning is typically measured by training and testing a model on the same distribution of data, i.e., the same domain. However, in real-world applications, models often encounter out-of-distribution data, such as novel camera viewpoints, backgrounds or image quality. The Visual Domain Adaptation (VisDA) challenge tests computer vision models’ ability to generalize and adapt to novel target distributions by measuring accuracy on out-of-distribution data.
The 2021 VisDA competition is our 5th time holding the challenge! [2017], [2018], [2019], [2020]. This year, we invite methods that can adapt to novel test distributions in an open-world setting. Teams will be given labeled source data from ImageNet and unlabeled target data from a different target distribution. In addition to input distribution shift, the target data may also have missing and/or novel classes as in the Universal Domain Adaptation (UniDA) setting [1]. Successful approaches will improve classification accuracy of known categories while learning to deal with missing and/or unknown categories.
So, I wrote a script based on a work by Geoffrey Chaucer and I got to the part about if it’s based on published work. Technically, it was but I can’t find the oldest publication and if it’s public domain, do I have to even say it’s based a published work?
Hi, new to DA.
Pretty much what the title said.
Also, have you ever seen a DA paper not showing the embedding visualization?
and how doubtful you will be when you see one?
Thanks.
i'm looking for a paper about DA ( both of Domain Adaptation, Data Augmentation)
i heard that paper has a table which shows the impact of each method of data Augmentation(Flip/Rotate/Shift etc....) on Domain Adaptation (i think that paper wants to tell data augmentation is enough for domain adaptation)
i searched on Arxiv, google with these keyword(domain adaptation, data augmentation, domain invarience etc...) but i couldn't find this paper
if you have good keyword or site or paper, recommend me please
sorry for bad english
Thank you.
Usually when bert is finetuned on downstream tasks (task adaptation) only small dataset (500 samples) is required. I am wondering how much data do we need to finetune it on a specific domain like Finance (domain adaptation) . And do you expect the resulting model to outperform original Bert on downstream tasks in Financial domain ? Thanks in advance..
https://medium.com/nerd-for-tech/domain-adaptation-problems-in-machine-learning-ddfdff1f227c
A dataset of ~22,500 labeled documents across four different domains. You can find it here:
It's a dog, it's a Nick Cage ... it's StyleGAN-NADA
How insane does it sound to describe a GAN with text (e.g. Human -> Werewolf) and get a SOTA generator that synthesizes images corresponding to the provided text query in any domain?! Rinon Gal and colleagues leverage the semantic power of CLIP's text-image latent space to shift a pretrained generator to a new domain. All it takes is a natural text prompts and a few minutes of training. The domains that StyleGAN-NADA covers are outright bizzare (and creepily specific) - Fernando Botero Painting, Dog → Nicolas Cage (WTF 😂), and more.
Usually it is hard (or outright impossible) to obtain a large number of images from a specific domain required to train a GAN. One can leverage the information learned by Vision-Language models such as CLIP, yet applying these models to manipulate pretrained generators to synthesize out-of-domain images is far from trivial. The authors propose to use dual generators and an adaptive layer selection procedure to increase training stability. Unlike prior works StyleGAN-NADA works in zero-shot manner and automatically selects a subset of layers to update at each iteration.
Read the full paper digest or the blog post (reading time ~5 minutes) to learn about Cross-Domain Adversarial Learning, how Image Space Regularization helps improve the results, and what optimization targets are used in Sketch Your Own GAN.
Meanwhile, check out the paper digest poster by Casual GAN Papers!
[Full Explanation / Blog Post] [Arxiv] [Code]
More recent popular computer vision paper breakdowns:
>[3D-Inpainting]
>
>[Real-ESRGAN]
>
>[[Sketch Your Own GAN](https:
It's a dog, it's a Nick Cage ... it's StyleGAN-NADA
How insane does it sound to describe a GAN with text (e.g. Human -> Werewolf) and get a SOTA generator that synthesizes images corresponding to the provided text query in any domain?! Rinon Gal and colleagues leverage the semantic power of CLIP's text-image latent space to shift a pretrained generator to a new domain. All it takes is a natural text prompts and a few minutes of training. The domains that StyleGAN-NADA covers are outright bizzare (and creepily specific) - Fernando Botero Painting, Dog → Nicolas Cage (WTF 😂), and more.
Usually it is hard (or outright impossible) to obtain a large number of images from a specific domain required to train a GAN. One can leverage the information learned by Vision-Language models such as CLIP, yet applying these models to manipulate pretrained generators to synthesize out-of-domain images is far from trivial. The authors propose to use dual generators and an adaptive layer selection procedure to increase training stability. Unlike prior works StyleGAN-NADA works in zero-shot manner and automatically selects a subset of layers to update at each iteration.
Read the full paper digest or the blog post (reading time ~5 minutes) to learn about Cross-Domain Adversarial Learning, how Image Space Regularization helps improve the results, and what optimization targets are used in Sketch Your Own GAN.
Meanwhile, check out the paper digest poster by Casual GAN Papers!
[Full Explanation / Blog Post] [Arxiv] [Code]
More recent popular computer vision paper breakdowns:
>[3D-Inpainting]
>
>[Real-ESRGAN]
>
>[[Sketch Your Own GAN](https
Interested in Universal Domain Adaptation? Join the VisDA 2021 Neurips competition!
This challenge will test how well models can (1) adapt to several distribution shifts and (2) detect unknown unknowns.
Top 3 teams will win $$$$ (2$k 1st/ $500 2nd and 3rd) in the form of VISA gift cards.
More details (and registration) at the website and below
-----------------------------------------------------------------------------
Progress in machine learning is typically measured by training and testing a model on the same distribution of data, i.e., the same domain. However, in real-world applications, models often encounter out-of-distribution data, such as novel camera viewpoints, backgrounds or image quality. The Visual Domain Adaptation (VisDA) challenge tests computer vision models’ ability to generalize and adapt to novel target distributions by measuring accuracy on out-of-distribution data.
The 2021 VisDA competition is our 5th time holding the challenge! [2017], [2018], [2019], [2020]. This year, we invite methods that can adapt to novel test distributions in an open-world setting. Teams will be given labeled source data from ImageNet and unlabeled target data from a different target distribution. In addition to input distribution shift, the target data may also have missing and/or novel classes as in the Universal Domain Adaptation (UniDA) setting [1]. Successful approaches will improve classification accuracy of known categories while learning to deal with missing and/or unknown categories.
Sketch Your GAN domain adaptation
Want to quickly train an entire GAN that generates realistic images from just two quick sketches done by hand? Heng-Yu Wang and team got you covered! They propose a new method to fine-tune a GAN to a small set of user-provided sketches that determine the shapes and poses of the objects on the synthesized images. They use domain adversarial loss and different regularization methods to preserve the original model's diversity and image quality.
The authors motivate the necessity of their approach mainly with the fact that training conditional GANs from scratch is simply a lot of work: you need powerful GPUs, annotated data, careful alignment, and pre-processing. In order for an end-user to generate images of a cats in a specific pose a very large number of such images is required, however with the proposed approach only a couple of sketches and a pretrained GAN is needed to create a new GAN that synthesizes images resembling the shape and orientation of sketches, and retains the diversity and quality of the original model. The resulting models can be used for random sampling, latent space interpolation and photo editing.
Read the full paper digest or the blog post (reading time ~5 minutes) to learn about Cross-Domain Adversarial Learning, how Image Space Regularization helps improve the results, and what optimization targets are used in Sketch Your Own GAN.
Meanwhile, check out the paper digest poster by Casual GAN Papers!
[Full Explanation/ Blog Post] [Arxiv] [Code]
More recent popular computer vision paper breakdowns:
>[3D-Inpainting]
>
>[Real-ESRGAN]
>
>[SupCon]
Want to quickly train an entire GAN that generates realistic images from just two quick sketches done by hand? Heng-Yu Wang and team got you covered! They propose a new method to fine-tune a GAN to a small set of user-provided sketches that determine the shapes and poses of the objects on the synthesized images. They use domain adversarial loss and different regularization methods to preserve the original model's diversity and image quality.
The authors motivate the necessity of their approach mainly with the fact that training conditional GANs from scratch is simply a lot of work: you need powerful GPUs, annotated data, careful alignment, and pre-processing. In order for an end-user to generate images of a cats in a specific pose a very large number of such images is required, however with the proposed approach only a couple of sketches and a pretrained GAN is needed to create a new GAN that synthesizes images resembling the shape and orientation of sketches, and retains the diversity and quality of the original model. The resulting models can be used for random sampling, latent space interpolation and photo editing.
Read the full paper digest or the blog post (reading time ~5 minutes) to learn about Cross-Domain Adversarial Learning, how Image Space Regularization helps improve the results, and what optimization targets are used in Sketch Your Own GAN.
Meanwhile, check out the paper digest poster by Casual GAN Papers!
[Full Explanation/ Blog Post] [Arxiv] [Code]
More recent popular computer vision paper breakdowns:
>[3D-Inpainting]
>
>[Real-ESRGAN]
>
>[SupCon]
