What is GBlock?
GBlock is a type of residual block that is used in the GAN-TTS text-to-speech architecture. The purpose of GBlock is to assist the generator in producing raw audio, with the receptive field of G large enough to capture long-term dependencies. In a GBlock, dilated convolutions are used to ensure the audio sequence contains 48000 samples, or a 2s training clip.
How Does GBlock Work?
A GBlock is a stack of two residual blocks. There are four kernel size-3 convolutions used in ea
A Ghost Bottleneck is a specific type of skip connection block used in the GhostNet CNN architecture. Similar to the basic residual block in ResNet, it integrates several convolutional layers and shortcuts. However, instead of integrating basic residual blocks, the Ghost Bottleneck stacks Ghost Modules instead.
The Ghost Module Structure
The Ghost Module structure consists of two stacked Ghost modules. The first module acts as an expansion layer, increasing the number of channels. The ratio b
Global Context Block is an image model block that allows modeling long-range dependencies while still having a lightweight computation. It combines the simplified non-local block and the squeeze-excitation block to create a framework for effective global context modeling.
What is Global Context Modeling?
Global Context Modeling is a technique used in computer vision to enable machines to recognize objects in images effectively. It involves considering the entire image's context, rather than j
When dealing with deep neural networks, a key aspect is efficiently representing and processing multi-scale features. This is where the Hierarchical-Split Block comes in. It utilizes a series of split and concatenate connections within a single residual block to achieve this goal.
The Basics of Hierarchical-Split Block
The Hierarchical-Split Block operates by taking ordinary feature maps and splitting them into a certain number of groups (denoted by s) each group containing a certain number o
What is Inception-ResNet-v2-B?
Inception-ResNet-v2-B is an image model block used in the Inception-ResNet-v2 architecture, specifically for a 17 x 17 grid. This model block utilizes the concepts of Inception modules and grouped convolutions but also incorporates residual connections. In simpler terms, Inception-ResNet-v2-B is a way to process images and extract important features from them to make accurate predictions or classifications.
What are Inception modules?
Inception modules are a ty
Inception-ResNet-v2-C is a block model used for image processing in the Inception-ResNet-v2 architecture. This block model is designed to work with an 8 x 8 grid and is based on the idea of Inception modules and grouped convolutions. In addition, Inception-ResNet-v2-C also includes residual connections, making it a comprehensive and robust image model block.
What is Inception-ResNet-v2?
Inception-ResNet-v2 is a deep neural network architecture designed for image recognition and classification
The Inverted Residual Block, also known as an MBConv Block, is a type of residual block used for image models that follows an inverted structure for efficiency reasons. This type of block was originally proposed for the MobileNetV2 CNN architecture and has since been widely used for several mobile-optimized CNNs.
Traditional Residual Block Structure
A traditional Residual Block has a structure that starts with a wide input, which is then compressed with a 1x1 convolution to a narrower size, a
Audio generation has long been an area of interest in the field of deep learning. The MelGAN Residual Block is a convolutional residual block used in the MelGAN generative audio architecture, aimed to generate high-quality audio waveforms from mel-spectrogram input at high sampling rates.
What is a Residual Block?
A residual block is a shortcut connection from input to output, designed to overcome the issues of gradient vanishing or exploding. The residual connections provide an alternative a
What is a Non-Local Block in Neural Networks?
Neural networks are a type of machine learning algorithm. They are designed to recognize patterns and relationships in data, making them useful for tasks like image recognition, natural language processing, and speech recognition. One key component of neural networks is the use of blocks, which are modular units that perform specific operations on the input data.
A non-local block is one type of image block module used in neural networks. It is des
Machine learning has become a buzzword in the world of technology. It is a technique that teaches computers to learn from data, without being programmed to do so. The NVAE Encoder Residual Cell is a fundamental building block in the NVAE architecture for the encoder. It is a type of residual connection block that consists of two series of BN-Swish-Conv layers without changing the number of channels. Let's dive deeper into the NVAE Encoder Residual Cell.
What is Machine Learning?
Machine learn
NVAE Generative Residual Cell: Improving Generative Models
Generative modeling is the process of creating a model that can generate new data that is similar to a given dataset. Generative models are a powerful tool in machine learning, and have applications in image and speech synthesis, text generation, and more. One such generative model is the NVAE, or Neural Variational Autoencoder, which is a type of neural network that can learn to encode and decode data with improved accuracy.
What is
One-Shot Aggregation is a model block used for images that is an alternative to Dense Blocks. It was created as part of the VoVNet architecture. This block aggregates intermediate features by connecting each convolution layer by two-way connections. One way is connected to the subsequent layer to produce a feature with a larger receptive field while the other way is aggregated only once into the final output feature map.
What is One-Shot Aggregation?
One-Shot Aggregation is a way to process i
One-Shot Aggregation with an Identity Mapping and eSE is a technical term used in the field of computer vision and machine learning. This term represents a machine learning model block which is used for image classification. It enhances the process of One-shot aggregation with a residual connection and automatic feature learning to output an effective squeeze-and-excitation block.
What is One-Shot Aggregation (OSA)?
One-shot aggregation (OSA) is a building block that has been designed for con
The ParaNet Convolution Block is a type of convolutional block used in the encoder and decoder of the ParaNet text-to-speech architecture. This block is similar to the DV3 Convolution Block, but with some key differences that make it stand out.
What is a ParaNet Convolution Block?
A convolutional block is a set of operations performed on an input that is typically a matrix of values. These operations aim to extract features from the input that can be used for further analysis or processing. I
A Pyramidal Bottleneck Residual Unit is a type of neural network architecture that is designed to improve the performance of deep learning models. It is named after the way its shape gradually widens from the top downwards, similar to a pyramid structure. It was introduced as part of the PyramidNet architecture, which is a state-of-the-art deep learning model used for image classification and object recognition.
What is a Residual Unit?
Before we dive into the details of a Pyramidal Bottlenec
Overview of Pyramidal Residual Unit
Pyramidal Residual Unit is a newer type of residual unit that has been introduced as part of the PyramidNet architecture. The pyramid structure of this unit means that the number of channels gradually increases as the layer moves downwards.
What is a Residual Unit?
Before diving into Pyramidal Residual Units, it’s essential to understand what residual units are.
A Residual Unit is a type of neural network architecture that features a shortcut connection,
Res2Net Block is a popular image model block that constructs hierarchical residual-like connections within a single residual block. This block has been introduced in Res2Net CNN architecture to represent multi-scale features at a granular level and increase the receptive field for each network layer.
What are Res2Net Blocks?
Res2Net Blocks are image model blocks that construct hierarchical residual-like connections within one single residual block for creating Convolutional Neural Networks (C
The concept of Residual Blocks is a fundamental building block of deep learning neural networks. Introduced as part of the ResNet architecture, Residual Blocks provide an effective way to train deep neural networks.
What are Residual Blocks?
Residual Blocks are skip-connection blocks that learn residual functions with reference to the layer inputs instead of learning unreferenced functions. They let the stacked nonlinear layers fit another mapping of the input variable, denoted by ${x}$. The