Self-Supervised Learning
1749 papers with code • 10 benchmarks • 41 datasets
Self-Supervised Learning is proposed for utilizing unlabeled data with the success of supervised learning. Producing a dataset with good labels is expensive, while unlabeled data is being generated all the time. The motivation of Self-Supervised Learning is to make use of the large amount of unlabeled data. The main idea of Self-Supervised Learning is to generate the labels from unlabeled data, according to the structure or characteristics of the data itself, and then train on this unsupervised data in a supervised manner. Self-Supervised Learning is wildly used in representation learning to make a model learn the latent features of the data. This technique is often employed in computer vision, video processing and robot control.
Source: Self-supervised Point Set Local Descriptors for Point Cloud Registration
Image source: LeCun
Benchmarks
These leaderboards are used to track progress in Self-Supervised Learning
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Libraries
Use these libraries to find Self-Supervised Learning models and implementationsDatasets
CIFAR-10
CIFAR-100
STL-10
Tiny ImageNet
2D-3D-S
NSynth
AVA
CATER
MotionSense
COCO 10% labeled data
Latest papers with no code
HYPE: Hyperbolic Entailment Filtering for Underspecified Images and Texts
In an era where the volume of data drives the effectiveness of self-supervised learning, the specificity and clarity of data semantics play a crucial role in model training.
Self-supervised visual learning in the low-data regime: a comparative evaluation
Self-Supervised Learning (SSL) is a valuable and robust training methodology for contemporary Deep Neural Networks (DNNs), enabling unsupervised pretraining on a `pretext task' that does not require ground-truth labels/annotation.
Neural Modes: Self-supervised Learning of Nonlinear Modal Subspaces
We propose a self-supervised approach for learning physics-based subspaces for real-time simulation.
Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud
To this end, we introduce a sequencer that orders point cloud tokens to efficiently compute and utilize tokens proximity based on their indices during target and context selection.
MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis
We further scale up the MiM to large pre-training datasets with more than 10k volumes, showing that large-scale pre-training can further enhance the performance of downstream tasks.
S2DEVFMAP: Self-Supervised Learning Framework with Dual Ensemble Voting Fusion for Maximizing Anomaly Prediction in Timeseries
Traditional anomaly detection methods often face challenges in handling diverse data characteristics and variations in noise levels, resulting in limited effectiveness.
Additive Margin in Contrastive Self-Supervised Frameworks to Learn Discriminative Speaker Representations
Implementing these two modifications to SimCLR improves performance and results in 7. 85% EER on VoxCeleb1-O, outperforming other equivalent methods.
Non-Uniform Exposure Imaging via Neuromorphic Shutter Control
To address this challenge, we propose a novel Neuromorphic Shutter Control (NSC) system to avoid motion blurs and alleviate instant noises, where the extremely low latency of events is leveraged to monitor the real-time motion and facilitate the scene-adaptive exposure.
Text-dependent Speaker Verification (TdSV) Challenge 2024: Challenge Evaluation Plan
This document outlines the Text-dependent Speaker Verification (TdSV) Challenge 2024, which centers on analyzing and exploring novel approaches for text-dependent speaker verification.
Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior
The majority of existing hyperspectral anomaly detection (HAD) methods use the low-rank representation (LRR) model to separate the background and anomaly components, where the anomaly component is optimized by handcrafted sparse priors (e. g., $\ell_{2, 1}$-norm).
