Multiscale Attentive Image De-Raining Networks via Neural Architecture Search

Image De-raining Using a Conditional Generative Adversarial Network

Severe weather conditions such as rain and snow adversely affect the visual quality of images captured under such conditions thus rendering them useless for further usage and sharing. In addition, such degraded images drastically affect performance of vision systems. Hence, it is important to solve the problem of single image de-raining/de-snowing. However, this is a difficult problem to solve ...

Efficient Neural Architecture Search via Parameter Sharing

We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. In ENAS, a controller discovers neural network architectures by searching for an optimal subgraph within a large computational graph. The controller is trained with policy gradient to select a subgraph that maximizes the expected reward on a validation set. Meanwhile the model cor...

Eecient T Raining of Artiicial Neural Networks for Autonomous Navigation

The ALVINN (Autonomous Land Vehicle In a Neural Network) project addresses the problem of training arti cial neural networks in real time to perform di cult perception tasks. ALVINN is a back-propagation network designed to drive the CMU Navlab, a modi ed Chevy van. This paper describes the training techniques which allow ALVINN to learn in under 5 minutes to autonomously control the Navlab by ...

Irony Detection with Attentive Recurrent Neural Networks

Automatic Irony Detection refers to making computer understand the real intentions of human behind the ironic language. Much work has been done using classic machine learning techniques applied on various features. In contrast to sophisticated feature engineering, this paper investigates how the deep learning can be applied to the intended task with the help of word embedding. Three different d...

Image to Latex via Neural Networks