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 4351–4400 of 5044 papers

TitleStatusHype
Objectives Matter: Understanding the Impact of Self-Supervised Objectives on Vision Transformer Representations—0
Object-level Scene Deocclusion—0
Object-Oriented Model Learning through Multi-Level Abstraction—0
Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations?—0
OccFlowNet: Towards Self-supervised Occupancy Estimation via Differentiable Rendering and Occupancy Flow—0
OCC-MLLM-Alpha:Empowering Multi-modal Large Language Model for the Understanding of Occluded Objects with Self-Supervised Test-Time Learning—0
OCTCube-M: A 3D multimodal optical coherence tomography foundation model for retinal and systemic diseases with cross-cohort and cross-device validation—0
OctoPath: An OcTree Based Self-Supervised Learning Approach to Local Trajectory Planning for Mobile Robots—0
Offline Clustering Approach to Self-supervised Learning for Class-imbalanced Image Data—0
On Compressing Sequences for Self-Supervised Speech Models—0
On Data Scaling in Masked Image Modeling—0
On depth prediction for autonomous driving using self-supervised learning—0
On-Device Self-Supervised Learning of Low-Latency Monocular Depth from Only Events—0
On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors—0
On Diversity in Discriminative Neural Networks—0
One-bit Supervision for Image Classification: Problem, Solution, and Beyond—0
Knowledgeable Salient Span Mask for Enhancing Language Models as Knowledge Base—0
One Masked Model is All You Need for Sensor Fault Detection, Isolation and Accommodation—0
One Network Doesn't Rule Them All: Moving Beyond Handcrafted Architectures in Self-Supervised Learning—0
One Objective for All Models --- Self-supervised Learning for Topic Models—0
Understanding The Robustness of Self-supervised Learning Through Topic Modeling—0
One-shot Learning for Channel Estimation in Massive MIMO Systems—0
On feature representations for marmoset vocal communication analysis—0
On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis—0
Contrastive Self-Supervised Learning Leads to Higher Adversarial Susceptibility—0
On Improving the Algorithm-, Model-, and Data- Efficiency of Self-Supervised Learning—0
On Learning Discriminative Features from Synthesized Data for Self-Supervised Fine-Grained Visual Recognition—0
On Learning to Solve Cardinality Constrained Combinatorial Optimization in One-Shot: A Re-parameterization Approach via Gumbel-Sinkhorn-TopK—0
Online Adversarial Purification based on Self-supervised Learning—0
On Linear Separation Capacity of Self-Supervised Representation Learning—0
Online Continual Learning of End-to-End Speech Recognition Models—0
Online Feature Updates Improve Online (Generalized) Label Shift Adaptation—0
Online Self-Supervised Learning for Object Picking: Detecting Optimum Grasping Position using a Metric Learning Approach—0
Online Self-Supervised Deep Learning for Intrusion Detection Systems—0
On minimal variations for unsupervised representation learning—0
On Negative Sampling for Audio-Visual Contrastive Learning from Movies—0
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data—0
On Partial Prototype Collapse in the DINO Family of Self-Supervised Methods—0
On Scaling Contrastive Representations for Low-Resource Speech Recognition—0
On Size Generalization in Graph Neural Networks—0
From Local Structures to Size Generalization in Graph Neural Networks—0
On the Discriminability of Self-Supervised Representation Learning—0
On the duality between contrastive and non-contrastive self-supervised learning—0
On the Effectiveness of ASR Representations in Real-world Noisy Speech Emotion Recognition—0
On the Effectiveness of Equivariant Regularization for Robust Online Continual Learning—0
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning—0
On the Effectiveness of Sampled Softmax Loss for Item Recommendation—0
On the Effectiveness of Speech Self-supervised Learning for Music—0
On The Effects of Learning Views on Neural Representations in Self-Supervised Learning—0
On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting—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