Graph Contrastive Coding (GCC) is a self-supervised pre-training framework for graph neural networks. Its goal is to capture the universal network topological properties across multiple networks. GCC is designed to learn intrinsic and transferable structural representations of graphs.
What is GCC?
Graph Contrastive Coding is a self-supervised method for capturing the topological properties of graphs. GCC uses a pre-training task called subgraph instance discrimination, which is designed to wo
Inpainting: Filling in the Blanks
You may have experienced a moment when you viewed a photograph and wished that it was complete, but parts were missing or damaged. Inpainting is the process of computational image editing that fills in the missing or damaged parts, similar to the process of photo restoration. The technique is called inpainting because it replaces missing or damaged areas with data from the surrounding areas.
What is Inpainting?
Inpainting is a technique for generating the mi
IMGEP - An Overview of Population-Based Intrinsically Motivated Goal Exploration Algorithms
IMGEP, which stands for Population-Based Intrinsically Motivated Goal Exploration Algorithms, is a set of algorithms for teaching robots how to learn complex skills such as tool use. It involves the use of intrinsically motivated agents that explore their environment without any prior knowledge of it. The algorithm is based on the idea that intrinsically motivated agents can acquire knowledge in a more e
What is Jigsaw?
Jigsaw is a machine learning approach that is used to improve image recognition tasks in computer vision. It is a self-supervision approach that relies on jigsaw-like puzzles as the pretext task in order to learn image representations.
The idea behind Jigsaw is that by solving jigsaw-like puzzles using image patches, the model can learn to recognize and piece together different parts of an image, thereby building up an understanding of what each part means and how they relate t
Magnification Prior Contrastive Similarity: A Self-Supervised Pre-Training Method for Efficient Representation Learning
Magnification Prior Contrastive Similarity (MPCS) is a self-supervised pre-training method used to learn efficient representations without labels on histopathology medical images. In this method, the algorithm utilizes different magnification factors to learn features of an image without the need for external supervision. This technique has shown promise in improving the accur
Introduction to Mirror-BERT: A Simple Yet Effective Text Encoder
Language is the primary tool humans use to communicate, and it is not surprising that advancements in technology have led to great strides in natural language processing. Pretrained language models like BERT (Bidirectional Encoder Representations from Transformers) have been widely adopted and used to improve language-related tasks like language translation, sentiment analysis, and text classification. However, converting such mod
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
If you have ever heard the term "MoCo", you might be wondering what it means. MoCo stands for Momentum Contrast, which is a type of self-supervised learning algorithm. But what does that even mean? Let's break it down.
What is MoCo?
MoCo is a method for training computer programs to recognize and classify images or patches of data. Specifically, it uses a type of machine learning called unsupervised learning. This means that the program does not need explicit labels or instructions in order t
Overview of NPID (Non-Parametric Instance Discrimination)
If you're interested in artificial intelligence (AI) and how machines learn, you might have heard of NPID. But what is it, and how does it work?
NPID stands for Non-Parametric Instance Discrimination. It's a type of self-supervised learning used in AI research to learn representations of data. Essentially, it's a way for machines to learn how to identify and differentiate between different types of objects or concepts.
What is Self-Su
Introduction to ParamCrop: Revolutionizing Video Contrastive Learning
ParamCrop is a groundbreaking technology that is transforming the way contrastive learning is done in the video industry. It utilizes a parametric cubic cropping method, where a 3D cube is cropped from the input video, and applies a differentiable spatio-temporal cropping operation. This allows it to be trained simultaneously with the video backbone and adjust the cropping strategy on the fly, ultimately increasing the contra
Pretext-Invariant Representation Learning (PIRL)
Pretext-Invariant Representation Learning, also known as PIRL, is a method that is used to learn invariant representations based on pretext tasks. Essentially, PIRL is designed to create image representations that are similar to the representation of transformed versions of the same image, while being different from the representations of other images.
This technique is commonly used in a pretext task that involves solving jigsaw puzzles. By usi
Overview of PASE+
PASE+ is a new type of speech encoder that uses a combination of convolutional and neural network models. This encoder is designed to solve self-supervised problems without the need for manual annotations. The PASE+ speech encoder works by distorting input signals with random disturbances using an online speech distortion module. The neural network then uses this distorted speech data to learn and improve its performance.
PASE+ is a problem-agnostic speech encoder, meaning th
What is ReLIC?
ReLIC stands for Representation Learning via Invariant Causal Mechanisms, and is a type of self-supervised learning objective that allows for improved generalization guarantees. It does this by enforcing invariant prediction of proxy targets across augmentations through an invariance regularizer.
How Does ReLIC Work?
ReLIC works by using a proxy task loss and Kullback-Leibler (KL) divergence to calculate similarity scores. Concretely, it associates every datapoint with a label
RotNet is a computer vision technique developed to aid in the self-supervision approach of image representation learning. The technique involves predicting image rotations as the pretext task to generate reliable image representations. The self-supervision approach in RotNet reduces the need for human-annotated data and allows the model to learn from a dataset with minimal supervision, thus making it a useful tool in automated image classification, detection, and recognition.
How RotNet Works
Understanding SEER: A Self-Supervised Learning Approach
SEER, short for Self-supERvised, is an innovative machine learning approach that has successfully trained self-supervised models without any supervision. It uses random, uncurated images as data and trains RegNet-Y architectures with SwAV. This article will provide a deeper understanding of SEER, including its benefits and unique features.
What is Self-Supervised Learning?
Self-supervised learning is a type of machine learning where a m
SSDS: A Solution for High Accuracy Image Segmentation
When it comes to image processing, one crucial aspect is image segmentation. Image segmentation involves identifying and separating the objects in an image to allow for further analysis. This process is challenging due to the diverse nature of images, and manual segmentation is time-consuming and prone to errors. However, with advances in deep learning, it is now possible to automate this process using machine learning models, with the most
Overview of SimCLR
SimCLR is a popular framework for contrastive learning of visual representations. The framework is designed to learn representations by maximizing the agreement between different augmented views of the same data example via contrastive loss in the latent space. In simpler terms, it tries to learn how to recognize different versions of the same image by comparing them in a special way.
The SimCLR framework mainly consists of three components: a stochastic data augmentation mo