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 1001–1050 of 5044 papers

TitleStatusHype
A dual task learning approach to fine-tune a multilingual semantic speech encoder for Spoken Language Understanding—0
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features—0
DiffMM: Multi-Modal Diffusion Model for RecommendationCode2
Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations?—0
A Comprehensive Survey of Foundation Models in Medicine—0
How Should We Extract Discrete Audio Tokens from Self-Supervised Models?—0
Self-Supervised Representation Learning with Spatial-Temporal Consistency for Sign Language RecognitionCode1
Self-Supervised and Few-Shot Learning for Robust Bioaerosol Monitoring—0
POWN: Prototypical Open-World Node ClassificationCode0
Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation—0
Shelf-Supervised Cross-Modal Pre-Training for 3D Object DetectionCode0
SSTFB: Leveraging self-supervised pretext learning and temporal self-attention with feature branching for real-time video polyp segmentation—0
T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation—0
LASER: Learning by Aligning Self-supervised Representations of Speech for Improving Content-related TasksCode0
An Initial Investigation of Language Adaptation for TTS Systems under Low-resource ScenariosCode2
You Don't Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning—0
Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations—0
An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels—0
Attentive Merging of Hidden Embeddings from Pre-trained Speech Model for Anti-spoofing DetectionCode2
GenDistiller: Distilling Pre-trained Language Models based on an Autoregressive Generative Model—0
SCDNet: Self-supervised Learning Feature-based Speaker Change Detection—0
ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets—0
Emotional Conversation: Empowering Talking Faces with Cohesive Expression, Gaze and Pose Generation—0
GraphFM: A Comprehensive Benchmark for Graph Foundation ModelCode0
Self-supervised Learning of Neural Implicit Feature Fields for Camera Pose Refinement—0
It is Never Too Late to Mend: Separate Learning for Multimedia RecommendationCode0
From Chaos to Clarity: 3DGS in the Dark—0
SimSAM: Simple Siamese Representations Based Semantic Affinity Matrix for Unsupervised Image SegmentationCode0
Exploring Self-Supervised Multi-view Contrastive Learning for Speech Emotion Recognition with Limited Annotations—0
A deep cut into Split Federated Self-supervised LearningCode0
Sustainable self-supervised learning for speech representations—0
Object-level Scene Deocclusion—0
Revolutionizing Wireless Networks with Self-Supervised Learning: A Pathway to Intelligent Communications—0
Visual Representation Learning with Stochastic Frame Prediction—0
FaceGPT: Self-supervised Learning to Chat about 3D Human Faces—0
Higher-Order Spatial Information for Self-Supervised Place Cell Learning—0
Emotion-Aware Speech Self-Supervised Representation Learning with Intensity Knowledge—0
Graph-Based Bidirectional Transformer Decision Threshold Adjustment Algorithm for Class-Imbalanced Molecular Data—0
NeuroMoCo: A Neuromorphic Momentum Contrast Learning Method for Spiking Neural Networks—0
Transforming Heart Chamber Imaging: Self-Supervised Learning for Whole Heart Reconstruction and Segmentation—0
ProFeAT: Projected Feature Adversarial Training for Self-Supervised Learning of Robust Representations—0
Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning—0
Weakly Supervised Set-Consistency Learning Improves Morphological Profiling of Single-Cell ImagesCode0
Unlocking Telemetry Potential: Self-Supervised Learning for Continuous Clinical Electrocardiogram Monitoring—0
Emo-bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition—0
Time-Series JEPA for Predictive Remote Control under Capacity-Limited Networks—0
On the social bias of speech self-supervised models—0
Denoising-Aware Contrastive Learning for Noisy Time SeriesCode1
Joint Spatial-Temporal Modeling and Contrastive Learning for Self-supervised Heart Rate Measurement—0
The Brain's Bitter Lesson: Scaling Speech Decoding With 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