Corpus ID: 29476958

Self corrective Perturbations for Semantic Segmentation and Classification

@article{Sankaranarayanan2017SelfCP,
  title={Self corrective Perturbations for Semantic Segmentation and Classification},
  author={S. Sankaranarayanan and Arpit Jain and S. Lim},
  journal={arXiv: Computer Vision and Pattern Recognition},
  year={2017}
}
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems. However, the behavior of deep networks is yet to be fully understood and is still an active area of research. In this work, we present an intriguing behavior: pre-trained CNNs can be made to improve their predictions by structurally perturbing the input. We observe that these… Expand

References

SHOWING 1-10 OF 18 REFERENCES
Conditional Random Fields as Recurrent Neural Networks
TLDR
A new form of convolutional neural network that combines the strengths of Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs)-based probabilistic graphical modelling is introduced, and top results are obtained on the challenging Pascal VOC 2012 segmentation benchmark. Expand
Fully convolutional networks for semantic segmentation
TLDR
The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. Expand
The Pascal Visual Object Classes Challenge: A Retrospective
TLDR
A review of the Pascal Visual Object Classes challenge from 2008-2012 and an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges. Expand
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
TLDR
This work addresses the task of semantic image segmentation with Deep Learning and proposes atrous spatial pyramid pooling (ASPP), which is proposed to robustly segment objects at multiple scales, and improves the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. Expand
Gaussian Conditional Random Field Network for Semantic Segmentation
TLDR
A novel deep network, which is referred to as Gaussian Mean Field (GMF) network, whose layers perform mean field inference over a Gaussian CRF, which outperforms various recent semantic segmentation approaches that combine CNNs with discrete CRF models. Expand
Multi-Scale Context Aggregation by Dilated Convolutions
TLDR
This work develops a new convolutional network module that is specifically designed for dense prediction, and shows that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. Expand
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
TLDR
This work takes convolutional neural networks trained to perform well on either the ImageNet or MNIST datasets and finds images with evolutionary algorithms or gradient ascent that DNNs label with high confidence as belonging to each dataset class, and produces fooling images, which are then used to raise questions about the generality of DNN computer vision. Expand
Explaining and Harnessing Adversarial Examples
TLDR
It is argued that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature, supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Expand
ImageNet Large Scale Visual Recognition Challenge
TLDR
The creation of this benchmark dataset and the advances in object recognition that have been possible as a result are described, and the state-of-the-art computer vision accuracy with human accuracy is compared. Expand
Understanding Neural Networks Through Deep Visualization
TLDR
This work introduces several new regularization methods that combine to produce qualitatively clearer, more interpretable visualizations of convolutional neural networks. Expand
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