Self-Supervised Learning

1688 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

Libraries

Use these libraries to find Self-Supervised Learning models and implementations
14 papers
2,716
11 papers
1,347
7 papers
3,062
See all 9 libraries.

GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised Learning

xiaojieli0903/genview 18 Mar 2024

To tackle these challenges, we present GenView, a controllable framework that augments the diversity of positive views leveraging the power of pretrained generative models while preserving semantics.

5
18 Mar 2024

A Versatile Framework for Multi-scene Person Re-identification

isee-laboratory/versreid 17 Mar 2024

To overcome significant variations between images across camera views, mountains of variants of ReID models were developed for solving a number of challenges, such as resolution change, clothing change, occlusion, modality change, and so on.

19
17 Mar 2024

Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples

cgcl-codes/gen-af 16 Mar 2024

In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models.

2
16 Mar 2024

Self-Supervised Learning for Time Series: Contrastive or Generative?

dl4mhealth/ssl_comparison 14 Mar 2024

In this paper, we will present a comprehensive comparative study between contrastive and generative methods in time series.

9
14 Mar 2024

LAFS: Landmark-based Facial Self-supervised Learning for Face Recognition

FaceOnLive/Face-Recognition-SDK-Android 13 Mar 2024

This enables our method - namely LAndmark-based Facial Self-supervised learning LAFS), to learn key representation that is more critical for face recognition.

201
13 Mar 2024

SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection

luy0222/sirst-5k 8 Mar 2024

The quality, quantity, and diversity of the infrared dataset are critical to the detection of small targets.

9
08 Mar 2024

Self-Supervision in Time for Satellite Images(S3-TSS): A novel method of SSL technique in Satellite images

hewanshrestha/why-self-supervision-in-time 7 Mar 2024

With the limited availability of labeled data with various atmospheric conditions in remote sensing images, it seems useful to work with self-supervised algorithms.

0
07 Mar 2024

Self-supervised Photographic Image Layout Representation Learning

cv-xueba/image-layout-learning 6 Mar 2024

This shortfall makes the learning process for photographic image layouts suboptimal.

0
06 Mar 2024

FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models

201younghanlee/flguard 5 Mar 2024

However, recent research proposed poisoning attacks that cause a catastrophic loss in the accuracy of the global model when adversaries, posed as benign clients, are present in a group of clients.

1
05 Mar 2024