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 451–500 of 6661 papers

TitleStatusHype
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy DataCode1
TAVGBench: Benchmarking Text to Audible-Video GenerationCode1
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense RetrievalCode1
ArtNeRF: A Stylized Neural Field for 3D-Aware Cartoonized Face SynthesisCode1
Blind Localization and Clustering of Anomalies in TexturesCode1
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time SeriesCode1
Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and NegativesCode1
InfoMatch: Entropy Neural Estimation for Semi-Supervised Image ClassificationCode1
RankCLIP: Ranking-Consistent Language-Image PretrainingCode1
WB LUTs: Contrastive Learning for White Balancing Lookup TablesCode1
UniSAR: Modeling User Transition Behaviors between Search and RecommendationCode1
Digging into contrastive learning for robust depth estimation with diffusion modelsCode1
Beyond Known Clusters: Probe New Prototypes for Efficient Generalized Class DiscoveryCode1
End-to-end training of Multimodal Model and ranking ModelCode1
Image-Text Co-Decomposition for Text-Supervised Semantic SegmentationCode1
Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive LearningCode1
Large Language Models for Expansion of Spoken Language Understanding Systems to New LanguagesCode1
ContrastCAD: Contrastive Learning-based Representation Learning for Computer-Aided Design ModelsCode1
Language Guided Domain Generalized Medical Image SegmentationCode1
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
Heterogeneous Contrastive Learning for Foundation Models and BeyondCode1
Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in ConversationCode1
Siamese Vision Transformers are Scalable Audio-visual LearnersCode1
OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental LearningCode1
EulerFormer: Sequential User Behavior Modeling with Complex Vector AttentionCode1
KDMCSE: Knowledge Distillation Multimodal Sentence Embeddings with Adaptive Angular margin Contrastive LearningCode1
HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text ClassificationCode1
Graph Augmentation for RecommendationCode1
GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph RepresentationCode1
GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity LearningCode1
Towards Principled Representation Learning from Videos for Reinforcement LearningCode1
Non-negative Contrastive LearningCode1
Do Generated Data Always Help Contrastive Learning?Code1
GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised LearningCode1
Open-World Semi-Supervised Learning for Node ClassificationCode1
Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential RecommendationCode1
Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling CasesCode1
EquiAV: Leveraging Equivariance for Audio-Visual Contrastive LearningCode1
Counterfactual contrastive learning: robust representations via causal image synthesisCode1
Dyadic Interaction Modeling for Social Behavior GenerationCode1
GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D UnderstandingCode1
Hyper-CL: Conditioning Sentence Representations with HypernetworksCode1
Decoupled Contrastive Learning for Long-Tailed RecognitionCode1
Style Blind Domain Generalized Semantic Segmentation via Covariance Alignment and Semantic Consistence Contrastive LearningCode1
Self-Adapting Large Visual-Language Models to Edge Devices across Visual ModalitiesCode1
Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation DistillationCode1
Inference via Interpolation: Contrastive Representations Provably Enable Planning and InferenceCode1
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal DecouplingCode1
A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive LearningCode1
Dual Mean-Teacher: An Unbiased Semi-Supervised Framework for Audio-Visual Source LocalizationCode1
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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