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Found 8 papers, 4 papers with code
Domain Separation Networks
However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain.
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Domain Adaptation
on Synth Objects-to-LINEMOD
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks.
Generative Adversarial Network
Unsupervised Domain Adaptation
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion
We provide a theoretical argument as to how the regularization is essential to our approach both for the case of single annotator and multiple annotators.
The Devil is in the Decoder: Classification, Regression and GANs
Many machine vision applications, such as semantic segmentation and depth prediction, require predictions for every pixel of the input image.
Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models
Training conditional maximum entropy models on massive data requires significant time and computational resources.
ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations
We introduce a new method for interpreting computer vision models: visually perceptible, decision-boundary crossing transformations.
Im2Calories: Towards an Automated Mobile Vision Food Diary
We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories.
Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth
In most machine learning tasks unambiguous ground truth labels can easily be acquired.
