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 2276–2300 of 6661 papers

TitleStatusHype
Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection—0
FESS Loss: Feature-Enhanced Spatial Segmentation Loss for Optimizing Medical Image AnalysisCode0
Contrastive Learning for Regression on Hyperspectral Data—0
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive LearningCode2
Topic Modeling as Multi-Objective Contrastive Optimization—0
SemTra: A Semantic Skill Translator for Cross-Domain Zero-Shot Policy Adaptation—0
Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image SegmentationCode4
Generalizing Conversational Dense Retrieval via LLM-Cognition Data AugmentationCode0
Rethinking Graph Masked Autoencoders through Alignment and UniformityCode0
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion RecognitionCode0
Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning—0
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive LossCode1
Learning Contrastive Feature Representations for Facial Action Unit DetectionCode0
Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain—0
Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning—0
Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging—0
CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation—0
Joint End-to-End Image Compression and Denoising: Leveraging Contrastive Learning and Multi-Scale Self-ONNs—0
Large Language Model Meets Graph Neural Network in Knowledge Distillation—0
Adaptive Hypergraph Network for Trust PredictionCode0
Multi-Patch Prediction: Adapting LLMs for Time Series Representation LearningCode2
Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance SegmentationCode1
Improved Generalization of Weight Space Networks via AugmentationsCode0
CAMBranch: Contrastive Learning with Augmented MILPs for Branching—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