**Anomaly Detection** is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types…
**Few-Shot Learning** is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to…
**Depth Estimation** is the task of measuring the distance of each pixel relative to the camera. Depth is extracted from either monocular (single) or stereo (multiple views of a scene) images. Traditional methods use multi-view geometry to find the relationship between the…
**Facial Recognition** is the task of making a positive identification of a face in a photo or video image against a pre-existing database of faces. It begins with detection - distinguishing human faces from other objects in the image - and then works on identification of those…
**Action Recognition** is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes. In the video domain, it is an open…
**Super-Resolution** is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while…
Autonomous driving is the task of driving a vehicle without human conduction. Many of the state-of-the-art results can be found at more general task pages such as [3D Object Detection](https://paperswithcode.com/task/3d-object-detection) and [Semantic…
Data augmentation involves techniques used for increasing the amount of data, based on different modifications, to expand the amount of examples in the original dataset. Data augmentation not only helps to grow the dataset but it also increases the diversity of the dataset. When…
**Domain Adaptation** is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized…
**Image Generation** (synthesis) is the task of generating new images from an existing dataset. - **Unconditional generation** refers to generating samples unconditionally from the dataset, i.e. $p(y)$ - **[Conditional image generation](/task/conditional-image-generation)**…
**Object Detection** is a computer vision task in which the goal is to detect and locate objects of interest in an image or video. The task involves identifying the position and boundaries of objects in an image, and classifying the objects into different categories. It forms a…
**Image Classification** is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike [object detection](/task/object-detection), which involves classification and location of multiple objects within an…
**Semantic Segmentation** is a computer vision task in which the goal is to categorize each pixel in an image into a class or object. The goal is to produce a dense pixel-wise segmentation map of an image, where each pixel is assigned to a specific class or object. Some example…
Or, How to Act Like You Know About the Biggest AI Development since CNNs
From predicting single sentence to fine-tuning using custom dataset to finding the best hyperparameter configuration.
Source normalization
Candidate pair generation and initial match scoring
Match scoring iteration
How Temporal Convolutional Networks are moving in favor of Sequence Modeling — Stock Trend Prediction.
Semantic segmentation is the task of predicting the class of each pixel in an image. This problem is more difficult than object detection…
Determining the optimal architectural parameters reduces network size by 84% while improving performance on natural-language-understanding tasks.
DuckDuckGo. Privacy, Simplified.
What are these new YOLO releases in 2020? How do they differ? Which one should I use?
Scientists have developed an Artificial Intelligence (AI) system that recognises hand gestures by combining skin-like electronics with computer vision.
Bringing structure to an unstructured task
Federated learning means enabling on-device training, model personalization, and more. Read more about it in this article.
What is Transfer Learning? Where can I use it? Why should I use it? How can I use it? Read On to find out!
Using Auto-Encoders to gain insight into data
443K subscribers in the learnmachinelearning community. A subreddit dedicated to learning machine learning
This is an overview of a great computer vision resource from Microsoft, which demonstrates best practices and implementation guidelines for a variety of tasks and scenarios.
AI researchers are using heartbeat detection to identify deepfake videos and even to figure out what kind of generative model created a deepfake.
How to set up and train a Yolo v5 Object Detection model?
Training Faster RNNs with Quasi-RNN
In Part 1 of this series, we introduced the concept of embedding vectors. In Part 2, we discussed how embedding vectors can be used in…
Through a joint collaboration, Papers with Code now provides category classification and code references for articles in the arXiv…
A review of 20+ deep learning NLP models and how to use them well
Machine learning is often fueled by image data. In this guide, learn the basics about image annotation, common techniques, and key workforce considerations.
PyTorch has sort of became one of the de facto standards for creating Neural Networks now, and I love its interface.
Here, I provide an in-depth analysis of GPUs for deep learning/machine learning and explain what is the best GPU for your use-case and budget.
In this post, we’ll create an end to end pipeline for image multiclass classification using Pytorch.This will include training the model, putting the model’s results in a form that can be shown to business partners, and functions to help deploy the model easily. As an added…
One infographic that explains how Reinforcement Learning, Deep Learning and Monte Carlo Search Trees are used in AlphaGo Zero.
Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science
AI researchers from the Ludwig Maximilian University (LMU) of Munich have developed a bite-sized text generator capable of besting OpenAI’s state of the art GPT-3 using only a tiny fraction of its parameters. GPT-3 is a monster of an AI sys
What does Microsoft getting an "exclusive license" to GPT-3 mean for the future of AI democratization?
Proximal Policy Optimization using RLlib-Ray
by creating them from scratch
Read this accessible and conversational article about understanding transformers, the data science way — by asking a lot of questions that is.
Which kinds of autoencoders exist and what are their applications?
Use a pre-trained neural network for feature extraction and cluster images using K-means.