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 2351–2375 of 6661 papers

TitleStatusHype
A General Purpose Supervisory Signal for Embodied Agents—0
GraphCL: Contrastive Self-Supervised Learning of Graph Representations—0
DARTS: A Dual-View Attack Framework for Targeted Manipulation in Federated Sequential Recommendation—0
DART: Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation—0
A Simple Framework for Uncertainty in Contrastive Learning—0
DarkFarseer: Inductive Spatio-temporal Kriging via Hidden Style Enhancement and Sparsity-Noise Mitigation—0
DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System—0
CLAR: Contrastive Learning of Auditory Representations—0
A Contrastive Learning Approach to Auroral Identification and Classification—0
DA-RAW: Domain Adaptive Object Detection for Real-World Adverse Weather Conditions—0
Damage GAN: A Generative Model for Imbalanced Data—0
DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection—0
A simple framework for contrastive learning phases of matter—0
Generalized Supervised Contrastive Learning—0
Contrastive Learning and Abstract Concepts: The Case of Natural Numbers—0
GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction—0
Introducing Depth into Transformer-based 3D Object Detection—0
D2CSE: Difference-aware Deep continuous prompts for Contrastive Sentence Embeddings—0
Self-supervised New Activity Detection in Sensor-based Smart Environments—0
Cycle Contrastive Adversarial Learning for Unsupervised image Deraining—0
Cycle-Contrast for Self-Supervised Video Representation Learning—0
CycleCL: Self-supervised Learning for Periodic Videos—0
CLAMP: Contrastive LAnguage Model Prompt-tuning—0
A contrastive-learning approach for auditory attention detection—0
CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset—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