Karthik Yearning Deep Learning

5 Intresting papers from Google AI - Nov01

  1. One Model To Learn Them All


    This paper demonstrates a single model to solve problems spanning from multiple domains. This model is trained on ImageNet , COCO dataset , a speech recognition corpus, and an English parsing task.


  2. Fluid Annotation


    A tool for image annotation. This is a model which performs a strong semantic segmentation, which a human annotator can modify through machine assisted edit operation. Fluid Annotation is a first exploratory step towards making image annotation faster and easier.


  3. A Neural Representation of Sketch Drawings


    In this paper, a sketch rnn is presented. A recurrent neural network able to construct stroke based drawings of common objects. The team has outlined a framework for conditional and unconditional sketch generation, and describe new robust training methods for generating coherent sketch drawings in a vector format.


  4. Neural Architecture Search with Reinforcement Learning


    In this paper, the team used a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set.


  5. Adversarial Spheres


    This paper is a study of misclassification images by the network which is close to correctly classified images. This is a study to solve the above mentioned adversarial perturbations of the input.


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