Domain adaptation is the task of adapting models between domains.
We address this question in the context of semantic visual navigation, which is the problem of a robot finding its way through a complex environment to a target object, e.g. go to the refrigerator. We propose to using high level semantic and contextual features including segmentation and detection masks obtained by off-the-shelf state-of- the-art vision as observations and use deep network to learn the navigation policy.
Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks. One appealing alternative is rendering synthetic data where ground-truth annotations are generated automatically.
However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain. Our novel architecture results in a model that outperforms the state-of-the-art on a range of unsupervised domain adaptation scenarios and additionally produces visualizations of the private and shared representations enabling interpretation of the domain adaptation process.
Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts.
We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks.
Furthermore, we present a new Multitask Question Answering Network (MQAN) jointly learns all tasks in decaNLP without any task-specific modules or parameters in the multitask setting. Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
DOMAIN ADAPTATION MACHINE TRANSLATION NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING RELATION EXTRACTION SEMANTIC PARSING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING
Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions.
#2 best model for Multimodal Unsupervised Image-To-Image Translation on Cats-and-Dogs
We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given function f, which accepts inputs in either domains, would remain unchanged.
#2 best model for Unsupervised Image-To-Image Translation on SVNH-to-MNIST
Given realistic frames as input, driving policy trained by reinforcement learning can nicely adapt to real world driving. To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data.
Generative adversarial networks have gained a lot of attention in general computer vision community due to their capability of data generation without explicitly modelling the probability density function and robustness to overfitting. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into the training and imposing higher order consistency that is proven to be useful in many cases, such as in domain adaptation, data augmentation, and image-to-image translation.