2020-12-25 · outsideonline.com

The Secret to Ultimate Fitness? Thru-Hiking.

The secret to ultimate fitness isn't all that complicated—just spend a month outside, hiking eight hours per day. Kyle Boelte breaks down how his body evolved into an efficient, fat-burning, testosterone-fueled machine over 29 days on the Colorado Trail.

2020-12-25 · yahoo.com

The Planks You Should Be Doing (But Probably Aren't)

As one-minute planks gave way to three-minute planks, and three-minute planks evolved into ten-minute planks, marathon plank-holding has become a calling card of sorts. " Being able to hold a traditional plank for 10 minutes isn't very functional for anything except just that,"…

2020-12-25 · youtube.com

Freestanding Handstand Push-Up Tutorial - YouTube

Hey guys, after working for 3 months on this I can finally release it! I tried to make this tutorial as detailed as possible and keep it entertaining at the same time! I hope I've succeeded at doing this and wish everyone a lot of luck with their journey. Please thumbs up…

2020-12-24 · slate.com

News

10 hours, 165 songs, one very wild year.

2020-12-24 · observablehq.com

Gallery / D3 / Observable

Looking for a good D3 example? Here’s a few (okay, …) to peruse. Animation D3’s data join, interpolators, and easings enable flexible animated transitions between views while preserving object constancy. Interaction D3’s low-level approach allows for performant incremental…

2020-12-21 · paperswithcode.com

Methodology | Papers With Code

**Transfer Learning** is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related…

2020-12-21 · paperswithcode.com

Methodology | Papers With Code

**Representation Learning** is a process in machine learning where algorithms extract meaningful patterns from raw data to create representations that are easier to understand and process. These representations can be designed for interpretability, reveal hidden features, or be…

2020-12-21 · paperswithcode.com

Object Tracking | Papers With Code

**Object tracking** is the task of taking an initial set of object detections, creating a unique ID for each of the initial detections, and then tracking each of the objects as they move around frames in a video, maintaining the ID assignment. State-of-the-art methods involve…

2020-12-21 · paperswithcode.com

Computer Vision | Papers With Code

**Image Retrieval** is a fundamental and long-standing computer vision task that involves finding images similar to a provided query from a large database. It's often considered as a form of fine-grained, instance-level classification. Not just integral to image recognition…

2020-12-21 · paperswithcode.com

Computer Vision | Papers With Code

**Zero-shot learning (ZSL)** is a model's ability to detect classes never seen during training. The condition is that the classes are not known during supervised learning. Earlier work in zero-shot learning use attributes in a two-step approach to infer unknown classes. In the…

2020-12-21 · paperswithcode.com

Computer Vision | Papers With Code

**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…

2020-12-21 · paperswithcode.com

Computer Vision | Papers With Code

**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…

2020-12-21 · paperswithcode.com

Computer Vision | Papers With Code

**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…

2020-12-21 · paperswithcode.com

Computer Vision | Papers With Code

**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…

2020-12-21 · paperswithcode.com

Computer Vision | Papers With Code

**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…

2020-12-21 · paperswithcode.com

Computer Vision | Papers With Code

**Denoising** is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of…