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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4926–4950 of 6661 papers

TitleStatusHype
Fine-grained Text to Image Synthesis—0
Fine-Grained Urban Flow Inference with Multi-scale Representation Learning—0
Fine-tuning the SwissBERT Encoder Model for Embedding Sentences and Documents—0
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations—0
Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation—0
FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization—0
FLAP: Fast Language-Audio Pre-training—0
FLAVARS: A Multimodal Foundational Language and Vision Alignment Model for Remote Sensing—0
FlowCLAS: Enhancing Normalizing Flow Via Contrastive Learning For Anomaly Segmentation—0
f-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning—0
FMiFood: Multi-modal Contrastive Learning for Food Image Classification—0
FMP: Toward Fair Graph Message Passing against Topology Bias—0
f-Mutual Information Contrastive Learning—0
FOAL: Fine-grained Contrastive Learning for Cross-domain Aspect Sentiment Triplet Extraction—0
Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition—0
Focus-Driven Contrastive Learning for Medical Question Summarization—0
Focus-Driven Contrastive Learning for Medical Question Summarization—0
FOCUS: Fine-grained Optimization with Semantic Guided Understanding for Pedestrian Attributes Recognition—0
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization—0
Foley-Flow: Coordinated Video-to-Audio Generation with Masked Audio-Visual Alignment and Dynamic Conditional Flows—0
FoMo: A Foundation Model for Mobile Traffic Forecasting with Diffusion Model—0
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning—0
Forget-me-not! Contrastive Critics for Mitigating Posterior Collapse—0
Forgetting Through Transforming: Enabling Federated Unlearning via Class-Aware Representation Transformation—0
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction—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