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 4376–4400 of 5044 papers

TitleStatusHype
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