Unsupervised Active Learning with Partial Learning


Unsupervised Active Learning with Partial Learning – A method for automatically learning to perform intelligent actions from video by optimizing the model-free training data of a given task is presented. Using a novel and fast learning algorithm, we show that a modified version of the KNN-based algorithm — K-Net — learns to perform the task effectively in a given environment, achieving state-of-the-art performance on the K-NN task when trained using only minimal data. We also show how the updated version can be used to learn to learn to perform this task effectively by directly optimizing the input data.

In this paper, we present a novel approach for segmentation of stereo images from natural images in order to make use of visual cues that affect the pixel-wise shape of the scene in images acquired in a low-resolution image. This approach aims to extract the image-level and semantic information from the image that can be used for joint segmentation. To solve this problem, we first analyze the two-dimensional image for the first and second-order features such as number and shape of joints. We then combine the two features into a single feature space in order to jointly segment the image from two images. We propose a new pixel-wise shape descriptor, which can be efficiently used for joint segmentation. The proposed model will be able to recover high-resolution stereo images from natural images. The proposed method is evaluated on our ImageNet dataset consisting of 90000 images acquired from natural images. The results indicate that our proposed approach is superior to other methods.

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Unsupervised Active Learning with Partial Learning

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  • Recurrent Neural Networks with Unbounded Continuous Delays for Brain Tractography Image Reconstruction

    Sparse and Robust Subspace Segmentation using Stereo MatchingIn this paper, we present a novel approach for segmentation of stereo images from natural images in order to make use of visual cues that affect the pixel-wise shape of the scene in images acquired in a low-resolution image. This approach aims to extract the image-level and semantic information from the image that can be used for joint segmentation. To solve this problem, we first analyze the two-dimensional image for the first and second-order features such as number and shape of joints. We then combine the two features into a single feature space in order to jointly segment the image from two images. We propose a new pixel-wise shape descriptor, which can be efficiently used for joint segmentation. The proposed model will be able to recover high-resolution stereo images from natural images. The proposed method is evaluated on our ImageNet dataset consisting of 90000 images acquired from natural images. The results indicate that our proposed approach is superior to other methods.


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