- Capsule Network
- Capsule Neural Networks
- Causal Inference
- Character Recognition
- Classification
- Clustering Analysis
- Co-Active Learning
- Co-Training
- Cognitive Architecture
- Cognitive Computing
- Collaborative Filtering
- Combinatorial Optimization
- Common Sense Reasoning
- Compositional Pattern-Producing Networks (CPPNs)
- Computational Creativity
- Computer Vision
- Concept Drift
- Concept Learning
- Constrained Optimization
- Content-Based Recommender Systems
- Contextual Bandits
- Contrastive Divergence
- Contrastive Learning
- Conversational Agents
- Convolutional Autoencoder
- Convolutional Encoder-Decoder Network
- Convolutional Long Short-Term Memory
- Convolutional Long Short-Term Memory (ConvLSTM)
- Convolutional Neural Gas
- Convolutional Neural Network
- Convolutional Recurrent Neural Network
- Convolutional Sparse Autoencoder
- Convolutional Sparse Coding
- Cross entropy loss
- Crossover
- Curriculum Learning
- Cyber Physical System
- Cyclical Learning Rate
What is Convolutional Encoder-Decoder Network
A Convolutional Encoder-Decoder Network is one of the modern methods for solving a wide range of computer vision tasks. As the name implies, the network consists of two main parts, namely encoder and decoder, each comprising a series of convolutional layers. In the following sections, we will go in-depth about the Convolutional Encoder-Decoder Network.
Encoder Network
The encoder network is responsible for converting the input image into an encoded form that retains the most important and relevant information of the image. An encoder network comprises multiple convolutional blocks, where each block contains several convolutional layers with a pooling layer to reduce the spatial dimensions of the feature maps. The number of convolutional blocks varies between different networks and depends on the complexity of the task.
When feeding the image through the encoder network, the output from each convolutional block is passed to the next block after the pooling layer, which reduces the dimensionality of the feature maps. The output from the last convolutional block results in a compressed representation of the input image, often referred to as a feature map or an encoding vector. The encoding vector is a higher-level representation of the input image, retaining only the main features and discarding the rest.
Decoder Network
The decoder network is responsible for reconstructing the input image from the encoded representation. The decoder network comprises several convolutional blocks, where each block contains several convolutional layers with an upsampling layer to restore the spatial dimensions of the feature maps. The number of convolutional blocks and the level of upsampling varies between different networks and depends on the complexity of the task.
The decoder network receives the encoded representation outputted from the encoder network and feeds it into the first convolutional block. The output of each convolutional block is passed to the next block after an upsampling layer, which expands the resolution of the feature maps. The aim of the decoder network is to reconstruct the input image as accurately as possible while retaining the significant features in the compressed form of the image.
Convolutional Encoder-Decoder Architectures
There are several Convolutional Encoder-Decoder architectures that are widely used in computer vision tasks, each with its unique characteristics and performance. In the following, we will go through some of the most popular architectures in the field.
- U-Net: U-Net architecture is introduced by Ronneberger et al. in 2015. It is mainly used for semantic segmentation tasks. The architecture consists of an encoder network, where the spatial dimensions of the feature maps decrease until the lowest dimension, and a decoder network, where the spatial dimensions of the feature maps increase to the original resolution of the input image.
- SegNet: SegNet architecture is introduced by Badrinarayanan et al. in 2017. It is mainly used for semantic segmentation tasks. The architecture consists of an encoder network, where the spatial dimensions of the feature maps decrease until the lowest dimension, and a decoder network, where the spatial dimensions of the feature maps reconstruct the input image. The significant difference between SegNet and U-Net is how they perform the upsampling step in the decoder network. SegNet uses pooling indices from the corresponding encoder block to upsample the feature maps by choosing the pixel locations where the max pooling operation was performed in the encoder block.
- DeepLabV3: DeepLabV3 architecture is introduced by Chen et al. in 2018. It is mainly used for semantic and instance segmentation tasks. The architecture consists of an encoder network, where the spatial dimensions of the feature maps decrease until the lowest dimension, a middle network, where several convolutional layers are applied to the output of the encoder network, and a decoder network, where the spatial dimensions of the feature maps increase to the original resolution of the input image.
- Conditional GAN: Conditional GAN architecture is introduced by Mirza et al. in 2014. It is mainly used for image synthesis tasks. The architecture consists of a generator network and a discriminator network. The generator network is a decoder network, where an encoded representation is fed and outputs a synthesized image. The discriminator network is a convolutional neural network that receives both a real image and a synthesized image and aims to distinguish them. The training of the network involves a min-max game, where the generator network tries to synthesize images that have the same statistical properties as those in the dataset, and the discriminator network aims to distinguish between the real and synthetic images.
Applications of Convolutional Encoder-Decoder Network
Convolutional Encoder-Decoder Network has been extensively used in various computer vision tasks, some of which are:
- Image Denoising: Removing noise from the image while preserving the salient features.
- Image Super-Resolution: Enhancing the quality of a low-resolution image to a high-resolution image.
- Image Segmentation: Dividing an image into different segments according to the objects and structures therein
- Object Detection: We want to detect the presence of an object in the given image.
- Image Synthesis: To generate synthetic images that are similar to those in the dataset.
Conclusion
Convolutional Encoder-Decoder Network has proved to be an efficient and versatile method for solving a wide range of computer vision tasks. Its ability to compress the input image into a compressed representation while retaining only the significant features and subsequently reconstructing the image back is a hallmark of its success. While there are several Convolutional Encoder-Decoder architectures available, their effectiveness and performance often depend on the task they are used for.