SOTAVerified

Self-Supervised Learning

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

Papers

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Showing 2351–2400 of 5044 papers

TitleStatusHype
IMU Based Deep Stride Length Estimation With Self-Supervised Learning—0
Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight—0
A Theoretical Characterization of Optimal Data Augmentations in Self-Supervised Learning—0
Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions—0
Improving Scene Graph Classification by Exploiting Knowledge from Texts—0
Improving Ultrasound Tongue Image Reconstruction from Lip Images Using Self-supervised Learning and Attention Mechanism—0
Improving Transformer-based Sequential Recommenders through Preference Editing—0
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition—0
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy—0
An ASR-free Fluency Scoring Approach with Self-Supervised Learning—0
Improving Streaming Transformer Based ASR Under a Framework of Self-supervised Learning—0
Improving Speech Inversion Through Self-Supervised Embeddings and Enhanced Tract Variables—0
Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning—0
Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data—0
Improving Sentence Representations with Consensus Maximisation—0
Improving self-supervised representation learning via sequential adversarial masking—0
Backdoor Attacks in the Supply Chain of Masked Image Modeling—0
An Analysis of Linear Complexity Attention Substitutes with BEST-RQ—0
D2SA: Dual-Stage Distribution and Slice Adaptation for Efficient Test-Time Adaptation in MRI Reconstruction—0
CycleCL: Self-supervised Learning for Periodic Videos—0
AWEncoder: Adversarial Watermarking Pre-trained Encoders in Contrastive Learning—0
Analyzing the factors affecting usefulness of Self-Supervised Pre-trained Representations for Speech Recognition—0
Adaptive Crowdsourcing Via Self-Supervised Learning—0
Improving Pre-trained Self-Supervised Embeddings Through Effective Entropy Maximization—0
Improving out-of-distribution generalization via multi-task self-supervised pretraining—0
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions—0
Improving Object Detection with Selective Self-supervised Self-training—0
Improving Node Representation by Boosting Target-Aware Contrastive Loss—0
Avoid Overthinking in Self-Supervised Models for Speech Recognition—0
Improving Masked Autoencoders by Learning Where to Mask—0
AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Video Deepfake Detection—0
Analyzing Speech Unit Selection for Textless Speech-to-Speech Translation—0
Improving Lesion Segmentation in Medical Images by Global and Regional Feature Compensation—0
Improving label efficiency through multi-task learning on auditory data—0
Custom Object Detection via Multi-Camera Self-Supervised Learning—0
Curriculum Learning Meets Weakly Supervised Modality Correlation Learning—0
Improving Graph Contrastive Learning via Adaptive Positive Sampling—0
Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning—0
CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments—0
Adapting self-supervised models to multi-talker speech recognition using speaker embeddings—0
CUBE360: Learning Cubic Field Representation for Monocular 360 Depth Estimation for Virtual Reality—0
Towards domain-invariant Self-Supervised Learning with Batch Styles Standardization—0
Improving Cross-Lingual Phonetic Representation of Low-Resource Languages Through Language Similarity Analysis—0
CSSL-RHA: Contrastive Self-Supervised Learning for Robust Handwriting Authentication—0
A vector quantized masked autoencoder for audiovisual speech emotion recognition—0
CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition—0
Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning—0
CSSL: Contrastive Self-Supervised Learning for Dependency Parsing on Relatively Free Word Ordered and Morphologically Rich Low Resource Languages—0
Analytic Study of Text-Free Speech Synthesis for Raw Audio using a Self-Supervised Learning Model—0
Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-supervised Learning—0
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All datasetsDABSSTL-10CIFAR10cifar100ImageNet-100 (TEMI Split)TinyImageNetCIFAR-10CIFAR-100CREMA-DTiny ImageNet

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pretraining: NoneImages & Text57.5—Unverified
2Pretraining: ShEDImages & Text54.3—Unverified
3Pretraining: e-MixImages & Text48.9—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50Accuracy91.7—Unverified
2ResNet18Accuracy91.02—Unverified
3MV-MRAccuracy89.67—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy93.89—Unverified
2ResNet18average top-1 classification accuracy92.58—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy72.51—Unverified
2ResNet18average top-1 classification accuracy69.31—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy82.64—Unverified
2CorInfomax (ResNet18)Top-1 Accuracy80.48—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy51.84—Unverified
2ResNet18average top-1 classification accuracy51.67—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy93.18—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy71.61—Unverified
#ModelMetricClaimedVerifiedStatus
1Hybrid BYOL-S/CvTAccuracy67.2—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy54.86—Unverified