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 601–625 of 6661 papers

TitleStatusHype
Beyond Co-occurrence: Multi-modal Session-based RecommendationCode1
Contrastive Learning for Compact Single Image DehazingCode1
Beyond Known Clusters: Probe New Prototypes for Efficient Generalized Class DiscoveryCode1
Adaptive Supervised PatchNCE Loss for Learning H&E-to-IHC Stain Translation with Inconsistent Groundtruth Image PairsCode1
Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure LearningCode1
Contrastive Learning for Knowledge TracingCode1
BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial LearningCode1
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input RepresentationCode1
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NERCode1
Contrastive Learning for Unpaired Image-to-Image TranslationCode1
AD-CLIP: Adapting Domains in Prompt Space Using CLIPCode1
Contrastive Learning for Weakly Supervised Phrase GroundingCode1
Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging DataCode1
Big Self-Supervised Models Advance Medical Image ClassificationCode1
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine TranslationCode1
AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative AdversariesCode1
Contrastive Fine-grained Class Clustering via Generative Adversarial NetworksCode1
Contrastive Denoising Score for Text-guided Latent Diffusion Image EditingCode1
Contrastive Deep SupervisionCode1
Contrastive learning of global and local features for medical image segmentation with limited annotationsCode1
Contrastive Learning of Medical Visual Representations from Paired Images and TextCode1
Contrastive Embeddings for Neural ArchitecturesCode1
Contrastive Grouping with Transformer for Referring Image SegmentationCode1
From t-SNE to UMAP with contrastive learningCode1
Contrastive Learning and Mixture of Experts Enables Precise Vector EmbeddingsCode1
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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