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Self-supervised Learning

A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches
Existing approaches for low-resource text summarization primarily employ large language models (LLMs) like GPT-3 or GPT-4 at inference …
EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision
This paper presents EarthView, a comprehensive dataset specifically designed for self-supervision on remote sensing data, intended to …
Few-shot Learning for Sign Language Recognition with Embedding Propagation
Sign language is a primary channel for the deaf and hard-hearing to communicate. Sign language consists of many signs with different …
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning
Handling out-of-distribution (OOD) samples has become a major stake in the real-world deploy- ment of machine learning systems. This …
Affinity Learning With Blind-spot Self-supervision for Image Denoising
In this paper, we extend the blind-spot based self-supervised denoising by using affinity learning to remove noise from affected …
Flaky Performances when Pretraining on Relational Databases
We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs …
RaVAEn: Unsupervised Change Detection of Extreme Events Using ML On-Board Satellites
Applications such as disaster management enormously benefit from rapid availability of satellite observations. Traditionally, data …
Flaky Performances when Pre-Training on Relational Databases with a Plan for Future Characterization Efforts
We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs …
A Survey of Self-Supervised and Few-Shot Object Detection
Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which …
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data
Remote sensing and automatic earth monitoring are key to solve global-scale challenges such as disaster prevention, land use …