SOTAVerified

Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

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Showing 3051–3100 of 6661 papers

TitleStatusHype
Label-free Prediction of Vascular Connectivity in Perfused Microvascular Networks in vitro—0
iEdit: Localised Text-guided Image Editing with Weak Supervision—0
Conversation Disentanglement with Bi-Level Contrastive Learning—0
CLIPPO: Image-and-Language Understanding from Pixels Only—0
CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy—0
Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning—0
Contrastive Learning is Just Meta-Learning—0
Label-invariant Augmentation for Semi-Supervised Graph Classification—0
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing—0
Image-free Domain Generalization via CLIP for 3D Hand Pose Estimation—0
GenCAD-Self-Repairing: Feasibility Enhancement for 3D CAD Generation—0
Image Prior and Posterior Conditional Probability Representation for Efficient Damage Assessment—0
Contrastive Learning in Memristor-based Neuromorphic Systems—0
GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors—0
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition—0
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling—0
Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture—0
Label-noise-tolerant medical image classification via self-attention and self-supervised learning—0
StackMix: A complementary Mix algorithm—0
LAC: Graph Contrastive Learning with Learnable Augmentation in Continuous Space—0
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels—0
GCC: Generative Calibration Clustering—0
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients—0
CoReFace: Sample-Guided Contrastive Regularization for Deep Face Recognition—0
Imitation from Observation With Bootstrapped Contrastive Learning—0
A dual contrastive framework—0
Gaze Estimation with Eye Region Segmentation and Self-Supervised Multistream Learning—0
iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition—0
Contrastive Learning Guided Latent Diffusion Model for Image-to-Image Translation—0
Contrastive Learning from Synthetic Audio Doppelgängers—0
CorMulT: A Semi-supervised Modality Correlation-aware Multimodal Transformer for Sentiment Analysis—0
BGM2Pose: Active 3D Human Pose Estimation with Non-Stationary Sounds—0
Bootstrapping Contrastive Learning Enhanced Music Cold-Start Matching—0
Multimodal Fusion and Coherence Modeling for Video Topic Segmentation—0
Improved baselines for vision-language pre-training—0
Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation—0
Gated Multimodal Fusion with Contrastive Learning for Turn-taking Prediction in Human-robot Dialogue—0
Contrastive Learning from Pairwise Measurements—0
Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models—0
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning—0
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System—0
GarmentAligner: Text-to-Garment Generation via Retrieval-augmented Multi-level Corrections—0
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning—0
Improved Forward-Forward Contrastive Learning—0
GANORCON: Are Generative Models Useful for Few-shot Segmentation?—0
Improve Unsupervised Pretraining for Few-label Transfer—0
GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning—0
Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning—0
Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment—0
Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation—0
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All datasetsimagenet-1k10,000 People - Human Pose Recognition DataCIFAR-10STL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6—Unverified
2ResNet50ImageNet Top-1 Accuracy73—Unverified
3ResNet50ImageNet Top-1 Accuracy71.1—Unverified
4ResNet50ImageNet Top-1 Accuracy69.3—Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6—Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8—Unverified
7ResNet50ImageNet Top-1 Accuracy63.6—Unverified
8ResNet50ImageNet Top-1 Accuracy61.5—Unverified
9ResNet50ImageNet Top-1 Accuracy61.5—Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3—Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55—Unverified