Abstract: We propose a UNet-based foundation model and its self-supervised learning method to address two key challenges: 1)lack of qualified annotated analog layout data, and 2)excessive variety in ...

Recent breakthroughs in self-supervised Joint-Embedding Predictive Architectures (JEPAs) have established that regularizing Euclidean representations toward isotropic Gaussian priors yields provable ...

In this video, we will study Supervised Learning with Examples. We will also look at types of Supervised Learning and its applications. Supervised learning is a type of Machine Learning which learns ...

We host our model on HuggingFace space, feel free to try our UnSAMv2 Demo! UnSAMv2 support granularity control for interactive image segmentation, whole image segmentation, and video segmentation.

Lab-grown “reductionist replicas” of the human brain are helping scientists understand fetal development and cognitive disorders, including autism. But ethical questions loom. Brain organoids, which ...

Labeling images is a costly and slow process in many computer vision projects. It often introduces bias and reduces the ability to scale large datasets. Therefore, researchers have been looking for ...

In this tutorial, we explore the power of self-supervised learning using the Lightly AI framework. We begin by building a SimCLR model to learn meaningful image representations without labels, then ...

Presentation of the best paper award at the RoboCup 2025 symposium. An important aspect of autonomous soccer-playing robots concerns accurate detection of the ball. This is the focus of work by Can ...

Abstract: Deep learning (DL) methods have been widely applied to synthetic aperture radar (SAR) land cover classification. The complexity of SAR data and the limited availability of labeled samples ...

This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...