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 2401–2450 of 5044 papers

TitleStatusHype
Enabling the Network to Surf the Internet—0
CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing—0
CASSL: Curriculum Accelerated Self-Supervised Learning—0
A Revisit of the Normalized Eight-Point Algorithm and A Self-Supervised Deep Solution—0
Adversarial Robustness of Discriminative Self-Supervised Learning in Vision—0
A Comparative Study of Voice Conversion Models with Large-Scale Speech and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge 2023—0
Multi-Granularity Click Confidence Learning via Self-Distillation in Recommendation—0
CaSS: A Channel-aware Self-supervised Representation Learning Framework for Multivariate Time Series Classification—0
Empirical Studies on the Convergence of Feature Spaces in Deep Learning—0
EMP: Enhance Memory in Data Pruning—0
A Review on Objective-Driven Artificial Intelligence—0
Emotion-Aware Speech Self-Supervised Representation Learning with Intensity Knowledge—0
Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotions—0
Cascade Network for Self-Supervised Monocular Depth Estimation—0
Emotional Conversation: Empowering Talking Faces with Cohesive Expression, Gaze and Pose Generation—0
Cascaded Self-supervised Learning for Subject-independent EEG-based Emotion Recognition—0
Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining—0
Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognition—0
EmoTalkingGaussian: Continuous Emotion-conditioned Talking Head Synthesis—0
CARL-G: Clustering-Accelerated Representation Learning on Graphs—0
EmoReg: Directional Latent Vector Modeling for Emotional Intensity Regularization in Diffusion-based Voice Conversion—0
A Review on Discriminative Self-supervised Learning Methods in Computer Vision—0
EmoGen: Eliminating Subjective Bias in Emotional Music Generation—0
Emo-bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition—0
CardOOD: Robust Query-driven Cardinality Estimation under Out-of-Distribution—0
CardiGraphormer: Unveiling the Power of Self-Supervised Learning in Revolutionizing Drug Discovery—0
A Review on Deep Learning Techniques for Video Prediction—0
A Comparative Study of Self-Supervised Speech Representations in Read and Spontaneous TTS—0
Mobility-Aware Federated Self-supervised Learning in Vehicular Network—0
Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network—0
Multi-Label Self-Supervised Learning with Scene Images—0
Emerging Property of Masked Token for Effective Pre-training—0
Captured by Captions: On Memorization and its Mitigation in CLIP Models—0
Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs—0
A Review of Predictive and Contrastive Self-supervised Learning for Medical Images—0
Embodied Self-supervised Learning by Coordinated Sampling and Training—0
Embodied Image Captioning: Self-supervised Learning Agents for Spatially Coherent Image Descriptions—0
Can We Ignore Labels In Out of Distribution Detection?—0
Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following—0
Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning—0
A Review of Machine Learning Methods Applied to Video Analysis Systems—0
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions—0
MultiCamCows2024 -- A Multi-view Image Dataset for AI-driven Holstein-Friesian Cattle Re-Identification on a Working Farm—0
Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling—0
Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer—0
Can Temporal Information Help with Contrastive Self-Supervised Learning?—0
Are foundation models efficient for medical image segmentation?—0
Efficient Training of Self-Supervised Speech Foundation Models on a Compute Budget—0
A Collective Learning Framework to Boost GNN Expressiveness—0
Multi-Airport Delay Prediction with Transformers—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