What is a Mixer Layer?
A Mixer layer is a layer that is used in the MLP-Mixer architecture designed for computer vision. The MLP-Mixer architecture was proposed by Tolstikhin et. al (2021) and is used in image recognition tasks. A Mixer layer is a type of layer that purely uses multi-layer perceptrons (MLPs) without using convolutions or attention. It is designed to take an input of embedded image patches (tokens) and generate an output with the same shape as its input. It functions in a simila
Overview of MLP-Mixer
The MLP-Mixer architecture, also known as Mixer, is an image architecture utilized for image classification tasks. What sets Mixer apart from other image architectures is that it doesn't rely on convolutions or self-attention to process images. Instead, Mixer uses multi-layer perceptrons (MLPs) that repeatedly apply across spatial locations or feature channels. This makes Mixer a unique and powerful image architecture.
How Mixer Works
At its core, Mixer takes a sequence
What is MnasNet?
MnasNet is a convolutional neural network that is particularly well-suited for mobile devices. It was discovered through neural architecture search, a process that uses algorithms to identify the best neural network structure for a particular task. In the case of MnasNet, the search algorithm took into account not only the accuracy of the network but also its latency, or the time it takes to complete a task. This means that MnasNet can achieve a good balance between accuracy an
Mobile Neural Network, also known as MNN, is a technology that has been specifically tailored to suit mobile applications. It is an inference engine that helps to improve computation and optimization on mobile devices.
What is Mobile Neural Network (MNN)?
Mobile Neural Network is a technology that is used to optimize mobile applications. It works by making use of deep neural networks to make predictions and classify data autonomously based on a set of rules. In other words, it is an artificia
Mobile periocular recognition is a technology used to identify individuals through the use of their eyes. In other words, it is a biometric recognition system based on the unique features of a person's eyes, including the shape, size, color, and texture of the skin around the eyes.
How Does Mobile Periocular Recognition Work?
The process of mobile periocular recognition typically involves capturing an image of a person's eyes using a mobile device such as a smartphone or tablet. This image is
Overview of MobileBERT
MobileBERT is a type of inverted-bottleneck BERT that compresses and accelerates the popular BERT model. This means that it takes the original BERT model - which is a powerful machine learning tool for natural language processing - and makes it smaller and faster.
Think of it like this: imagine you have a large library filled with books of different sizes and genres. If you want to quickly find a book on a specific topic, it might take you a while to navigate through all
MobileDet is an innovative object detection model designed specifically for mobile accelerators. This model extensively utilizes regular convolutions on EdgeTPUs and DSPs, particularly in the early stages of the network where depthwise convolutions can be less efficient. By doing so, it enhances the trade-off between latency and accuracy for object detection on mobile accelerators, provided they are placed strategically within the network by neural architecture search. This approach permits the
MobileNetV1: The Lightweight Convolutional Neural Network for Mobile and Embedded Vision Applications
MobileNetV1 is a type of convolutional neural network designed for mobile and embedded vision applications. It is based on a streamlined architecture that uses depthwise separable convolutions to build lightweight deep neural networks that can have low latency for mobile and embedded devices.
The Need for MobileNetV1
Traditional convolutional neural networks are large and computationally exp
MobileNetV2: A Mobile-Optimized Convolutional Neural Network
A convolutional neural network (CNN) is a type of deep learning algorithm designed to recognize patterns in visual data. CNNs have proven powerful in many computer vision tasks. However, their size and compute requirements make it challenging to use in mobile devices with limited resources. To address this issue, MobileNetV2 was developed - a CNN architecture aimed at mobile devices, which prioritizes efficiency without sacrificing ac
MobileNetV3 Overview: A Convolutional Neural Network for Mobile Phones
MobileNetV3 is a specialized convolutional neural network designed for use on mobile phone CPUs. This state-of-the-art network is made possible through a combination of advanced hardware-aware network architecture search technology (NAS) and the innovative NetAdapt algorithm. Furthermore, it has been improved through a range of novel architecture advances.
The Search Techniques Used in MobileNetV3
To ensure that MobileNet
MoBY is a cutting-edge approach in deep learning called self-supervised learning for Vision Transformers. It is a unique amalgamation of two previously existing techniques, MoCo v2 and BYOL, which has yielded remarkable results. The name MoBY is derived from the first two letters of each technique. It inherits the momentum design, the key queue, and the contrastive loss used in MoCo v2, and asymmetric encoders and momentum scheduler implemented in BYOL.
How does MoBY work?
The MoBY approach c
MoCo v2 is an enhanced version of the Momentum Contrast self-supervised learning algorithm. This algorithm is used to train models to recognize patterns in data without the need for labeled examples. This means that the model can learn to identify important patterns in data all on its own, without needing human assistance.
What Is Self-Supervised Learning?
Self-supervised learning is a type of machine learning where the model learns from the data it is given, rather than from labeled examples
Overview of MoCo v3
MoCo v3 is a training method used to improve the performance of self-supervised image recognition algorithms. It is an updated version of MoCo v1 and v2 that uses two crops of each image and random data augmentation to encode image features.
How MoCo v3 Works
MoCo v3 uses two encoders, $f_q$ and $f_k$, to encode two crops of each image. The encoders outputs are vectors $q$ and $k$ that are trained to work like a "query" and "key" pair. The goal of the training is to retri
Mode normalization is a technique used to normalize different modes of data on-the-fly. It extends the traditional normalization approach, which only considers a single mean and variance, to jointly normalize samples that share common features. This technique involves using a gating network to assign samples in a mini-batch to different modes, and then normalizing each sample with estimators for its corresponding mode.
What is Normalization?
Normalization is a technique widely used in machine
MAML or Model-Agnostic Meta-Learning is a powerful algorithm for meta-learning. It is model and task-agnostic, meaning it can be applied to any neural network and can be used for any task. The goal of MAML is to train a model's parameters in such a way that only a few gradient updates are required for fast learning of a new task.
How MAML Works
MAML is based on the idea of adapting a model's parameters to a new task quickly. The model is represented by a function, fθ, with parameters θ. When
MFEC stands for Memory-free Function Approximation with Continuous-kernel (C-k) dEcomposition. It is a non-parametric technique used to approximate Q-values that is based on storing all the visited states and then using k-Nearest Neighbors algorithm for inference.
Memory-free Function Approximation with Continuous-kernel (C-k) dEcomposition
MFEC is an approach that is characterized by the use of non-parametric methods to approximate Q-values. Q-value is a measure of the expected future reward
Overview of TinyNet
TinyNet is a technique for downsizing neural architectures through a series of smaller models derived from EfficientNet-B0 with the FLOPs constraint. This method explores the twisting rules for obtaining deep neural networks with minimum model sizes and computational costs while maintaining high efficiency and excellent performance.
EfficientNets
EfficientNets is a series of techniques designed for obtaining excellent deep neural architectures. The giant formula for enlar
Overview of Models Genesis: A Self-Supervised Approach for Learning 3D Image Representations
If you are interested in the field of medical imaging, you might have heard of a new technique called Models Genesis, or Generic Autodidactic Models. This technique is used for learning 3D image representations, and it has the potential to revolutionize the way we analyze medical images.
The idea behind Models Genesis is to learn a common image representation that can be used across diseases, organs, a