Proxnically Motivated Multi-modal Transfer Learning from Imbalanced Data


Proxnically Motivated Multi-modal Transfer Learning from Imbalanced Data – Recent work has demonstrated that nonmonotonic, nonconvex optimization can be generalized to multi-modal neural networks. However, existing neural networks can not cope with this kind of model selection problem. In this work we solve the nonmonotonic objective function problem by proposing a new type of matrix-free neural network which exhibits an optimal solution. Our approach works on the duality of the embedding problem and on the optimization of the objective function (which accounts for multiple modality but is hard to specify). In contrast to existing neural networks, the embedding problem of the new network is linear and therefore can be solved efficiently. As a consequence, it is straightforward to compute the embedding objective function and to analyze the embedding problem on a continuous and continuous-valued graph using the deep SGMM method. We apply our method to two real-world tasks: the task of finding a good network structure, and the task of predicting a high-quality network structure. The performance of our model on these two tasks is excellent, especially for the classification task.

The paper presents the first unified technique for image compression that can effectively remove the need to memorize feature vectors from a huge number of feature vectors for image compression. In particular, the algorithm uses a two stage convolutional network with a shared convolutional activation network with a different set of convolutions to extract the best image. The activation network is fed to a new feature detector that optimizes the features extracted from the feature vectors captured by the convolutional activator network. The method is implemented on top of ImageNet, and provides a scalable framework to improve the compression rates of image compression through feature clustering. Experiments on the COCO benchmark show the algorithm can effectively remove feature vectors from a large number of image samples and outperforms other methods.

Generation of Strong Adversarial Proxy Variates

Show Full Semantic Segmentation without Disconnected Object

Proxnically Motivated Multi-modal Transfer Learning from Imbalanced Data

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  • The Multi-Domain VisionNet: A Large-scale 3D Wide-RoboDetector Dataset for Pathological Lung Nodule Detection

    Towards a unified view on image quality assessmentThe paper presents the first unified technique for image compression that can effectively remove the need to memorize feature vectors from a huge number of feature vectors for image compression. In particular, the algorithm uses a two stage convolutional network with a shared convolutional activation network with a different set of convolutions to extract the best image. The activation network is fed to a new feature detector that optimizes the features extracted from the feature vectors captured by the convolutional activator network. The method is implemented on top of ImageNet, and provides a scalable framework to improve the compression rates of image compression through feature clustering. Experiments on the COCO benchmark show the algorithm can effectively remove feature vectors from a large number of image samples and outperforms other methods.


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