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 101–150 of 6661 papers

TitleStatusHype
Enriching Location Representation with Detailed Semantic Information—0
MotionRAG-Diff: A Retrieval-Augmented Diffusion Framework for Long-Term Music-to-Dance Generation—0
Uncertainty-Aware Metabolic Stability Prediction with Dual-View Contrastive Learning—0
AuralSAM2: Enabling SAM2 Hear Through Pyramid Audio-Visual Feature PromptingCode0
EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG—0
Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition—0
JojoSCL: Shrinkage Contrastive Learning for single-cell RNA sequence ClusteringCode0
A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning for Any Atlas and DisorderCode1
CLARIFY: Contrastive Preference Reinforcement Learning for Untangling Ambiguous QueriesCode0
M3ANet: Multi-scale and Multi-Modal Alignment Network for Brain-Assisted Target Speaker ExtractionCode0
A Cross Branch Fusion-Based Contrastive Learning Framework for Point Cloud Self-supervised Learning—0
Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic SegmentationCode0
A Mathematical Perspective On Contrastive Learning—0
Efficient Text Encoders for Labor Market Analysis—0
MGS3: A Multi-Granularity Self-Supervised Code Search Framework—0
Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation—0
BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive LearningCode0
Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation LearningCode0
FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series ClassificationCode1
Skin Lesion Phenotyping via Nested Multi-modal Contrastive Learning—0
CryoCCD: Conditional Cycle-consistent Diffusion with Biophysical Modeling for Cryo-EM Synthesis—0
EmotionRankCLAP: Bridging Natural Language Speaking Styles and Ordinal Speech Emotion via Rank-N-Contrast—0
Query Routing for Retrieval-Augmented Language Models—0
Bridging the Gap Between Semantic and User Preference Spaces for Multi-modal Music Representation Learning—0
GenCAD-Self-Repairing: Feasibility Enhancement for 3D CAD Generation—0
One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object TrajectoryCode2
Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action RecognitionCode0
Patient Domain Supervised Contrastive Learning for Lung Sound Classification Using Mobile Phone—0
Single Domain Generalization for Alzheimer's Detection from 3D MRIs with Pseudo-Morphological Augmentations and Contrastive LearningCode0
From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration—0
Improving Contrastive Learning for Referring Expression CountingCode0
Aligning Proteins and Language: A Foundation Model for Protein Retrieval—0
CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive LearningCode0
Supervised Contrastive Learning for Ordinal Engagement Measurement—0
Enhancing Contrastive Learning-based Electrocardiogram Pretrained Model with Patient Memory QueueCode0
LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense RetrievalCode0
Style2Code: A Style-Controllable Code Generation Framework with Dual-Modal Contrastive Representation LearningCode0
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
FruitNeRF++: A Generalized Multi-Fruit Counting Method Utilizing Contrastive Learning and Neural Radiance FieldsCode3
Can Visual Encoder Learn to See Arrows?—0
Hard Negative Contrastive Learning for Fine-Grained Geometric Understanding in Large Multimodal ModelsCode0
Modality Curation: Building Universal Embeddings for Advanced Multimodal Information RetrievalCode1
AmpleHate: Amplifying the Attention for Versatile Implicit Hate DetectionCode0
Distill CLIP (DCLIP): Enhancing Image-Text Retrieval via Cross-Modal Transformer Distillation—0
Conventional Contrastive Learning Often Falls Short: Improving Dense Retrieval with Cross-Encoder Listwise Distillation and Synthetic DataCode0
Paying Alignment Tax with Contrastive Learning—0
Learn Beneficial Noise as Graph Augmentation—0
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge CollaborationCode0
HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation—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