Partial Convolution based Padding
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Summary
This paper presents a simple yet effective padding scheme that can be used as a drop-in module for existing convolutional neural networks and demonstrates that the proposed padding scheme consistently outperforms standard zero padding with better accuracy.
- Type
- preprint
- Published
- 2018-11-28
- Cited by
- 100
- References
- 37
- Access
- Open access
- OpenAlex
- https://openalex.org/W2902451427
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:53809653
Keywords
Padding, Computer science, Convolutional neural network, Convolution (computer science), Segmentation
References
- ADADELTA: An Adaptive Learning Rate Method
- Learning from the Edge.
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- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- Going deeper with convolutions
- ImageNet classification with deep convolutional neural networks
- Shepard Convolutional Neural Networks
- Deep Residual Learning for Image Recognition
- Context Encoders: Feature Learning by Inpainting
- Semantic Image Inpainting with Perceptual and Contextual Losses
- Instance Normalization: The Missing Ingredient for Fast Stylization
- High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis
- Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
- Pyramid Scene Parsing Network
- Generative Face Completion
- Globally and locally consistent image completion
- Segmentation-Aware Convolutional Networks Using Local Attention Masks
- Sparsity Invariant CNNs
Cited by
- FOSNet: An End-to-End Trainable Deep Neural Network for Scene Recognition
- Distribution Padding in Convolutional Neural Networks
- PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments
- Segmentation of Surface Cracks Based on a Fully Convolutional Neural Network and Gated Scale Pooling
- Partial Convolution Based Multimodal Autoencoder for Art Investigation
- Image Restoration using Automatic Damaged Regions Detection and Machine Learning-Based Inpainting Technique
- Learning metal artifact reduction in cardiac CT images with moving pacemakers
- Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference
- The Impact of Hole Geometry on Relative Robustness of In-Painting Networks: An Empirical Study
- On Translation Invariance in CNNs: Convolutional Layers Can Exploit Absolute Spatial Location
- Masking and Inpainting: A Two-Stage Speech Enhancement Approach for Low SNR and Non-Stationary Noise
- Region Hiding for Image Inpainting via Single-Image Training of U-Net
- Panoptic-Based Image Synthesis
- Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter
- DEEP DOMAIN ADAPTATION BY WEIGHTED ENTROPY MINIMIZATION FOR THE CLASSIFICATION OF AERIAL IMAGES
- One shot 3D photography
- Deep learning in electron microscopy
- Painting Outside as Inside: Edge Guided Image Outpainting via Bidirectional Rearrangement with Step-By-Step Learning
- Mind the Pad - CNNs can Develop Blind Spots
- A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data Using Unstructured Spatial Discretizations
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