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 701–725 of 6661 papers

TitleStatusHype
MADGEN: Mass-Spec attends to De Novo Molecular generationCode1
Multimodal Contrastive Representation Learning in Augmented Biomedical Knowledge GraphsCode1
Few-shot Implicit Function Generation via Equivariance—0
AdaCrossNet: Adaptive Dynamic Loss Weighting for Cross-Modal Contrastive Point Cloud LearningCode0
Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation—0
MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector QuantizationCode3
An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise—0
Understanding Difficult-to-learn Examples in Contrastive Learning: A Theoretical Framework for Spectral Contrastive Learning—0
PromptHash:Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing RetrievalCode0
Relation3D : Enhancing Relation Modeling for Point Cloud Instance SegmentationCode1
V^2Dial: Unification of Video and Visual Dialog via Multimodal Experts—0
Adapting to Observation Length of Trajectory Prediction via Contrastive Learning—0
EASEMVC:Efficient Dual Selection Mechanism for Deep Multi-View Clustering—0
A Tale of Two Classes: Adapting Supervised Contrastive Learning to Binary Imbalanced Datasets—0
DyCON: Dynamic Uncertainty-aware Consistency and Contrastive Learning for Semi-supervised Medical Image Segmentation—0
CLOC: Contrastive Learning for Ordinal Classification with Multi-Margin N-pair Loss—0
Less Attention is More: Prompt Transformer for Generalized Category DiscoveryCode0
SLADE: Shielding against Dual Exploits in Large Vision-Language Models—0
Viewpoint Rosetta Stone: Unlocking Unpaired Ego-Exo Videos for View-invariant Representation Learning—0
ODA-GAN: Orthogonal Decoupling Alignment GAN Assisted by Weakly-supervised Learning for Virtual Immunohistochemistry StainingCode0
Bringing CLIP to the Clinic: Dynamic Soft Labels and Negation-Aware Learning for Medical Analysis—0
Dynamic Stereotype Theory Induced Micro-expression Recognition with Oriented Deformation—0
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution—0
Perceptual Inductive Bias Is What You Need Before Contrastive Learning—0
Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation—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