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 2001–2025 of 6661 papers

TitleStatusHype
BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive LearningCode0
From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration—0
Single Domain Generalization for Alzheimer's Detection from 3D MRIs with Pseudo-Morphological Augmentations and Contrastive LearningCode0
Improving Contrastive Learning for Referring Expression CountingCode0
Supervised Contrastive Learning for Ordinal Engagement Measurement—0
CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive LearningCode0
Aligning Proteins and Language: A Foundation Model for Protein Retrieval—0
Multimodal Reasoning Agent for Zero-Shot Composed Image Retrieval—0
A Contrastive Learning Foundation Model Based on Perfectly Aligned Sample Pairs for Remote Sensing Images—0
Enhancing Contrastive Learning-based Electrocardiogram Pretrained Model with Patient Memory QueueCode0
Style2Code: A Style-Controllable Code Generation Framework with Dual-Modal Contrastive Representation LearningCode0
Can Visual Encoder Learn to See Arrows?—0
LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense RetrievalCode0
AmpleHate: Amplifying the Attention for Versatile Implicit Hate DetectionCode0
Hard Negative Contrastive Learning for Fine-Grained Geometric Understanding in Large Multimodal ModelsCode0
Paying Alignment Tax with Contrastive Learning—0
Domain and Task-Focused Example Selection for Data-Efficient Contrastive Medical Image SegmentationCode0
Distill CLIP (DCLIP): Enhancing Image-Text Retrieval via Cross-Modal Transformer Distillation—0
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge CollaborationCode0
Conventional Contrastive Learning Often Falls Short: Improving Dense Retrieval with Cross-Encoder Listwise Distillation and Synthetic DataCode0
An Interpretable Representation Learning Approach for Diffusion Tensor Imaging—0
HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation—0
Learn Beneficial Noise as Graph Augmentation—0
AmorLIP: Efficient Language-Image Pretraining via AmortizationCode0
Manifold-aware Representation Learning for Degradation-agnostic Image Restoration—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