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 2901–2925 of 6661 papers

TitleStatusHype
Embed and Emulate: Contrastive representations for simulation-based inference—0
You Only Speak Once to See—0
CleanerCLIP: Fine-grained Counterfactual Semantic Augmentation for Backdoor Defense in Contrastive Learning—0
Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification—0
LoopSR: Looping Sim-and-Real for Lifelong Policy Adaptation of Legged Robots—0
Harnessing Shared Relations via Multimodal Mixup Contrastive Learning for Multimodal ClassificationCode0
Robotic-CLIP: Fine-tuning CLIP on Action Data for Robotic Applications—0
Self-supervised Pretraining for Cardiovascular Magnetic Resonance Cine SegmentationCode0
Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data—0
Hyperbolic Image-and-Pointcloud Contrastive Learning for 3D Classification—0
Selection of Prompt Engineering Techniques for Code Generation through Predicting Code Complexity—0
PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings—0
CLSP: High-Fidelity Contrastive Language-State Pre-training for Agent State Representation—0
DIAL: Dense Image-text ALignment for Weakly Supervised Semantic Segmentation—0
Towards Universal Large-Scale Foundational Model for Natural Gas Demand Forecasting—0
ManiNeg: Manifestation-guided Multimodal Pretraining for Mammography ClassificationCode0
Spatial-Temporal Mixture-of-Graph-Experts for Multi-Type Crime Prediction—0
ViKL: A Mammography Interpretation Framework via Multimodal Aggregation of Visual-knowledge-linguistic FeaturesCode0
Enhanced Unsupervised Image-to-Image Translation Using Contrastive Learning and Histogram of Oriented Gradients—0
Patch-Based Contrastive Learning and Memory Consolidation for Online Unsupervised Continual LearningCode0
Learning from Contrastive Prompts: Automated Optimization and Adaptation—0
Cross-Model Cross-Stream Learning for Self-Supervised Human Action RecognitionCode0
TS-HTFA: Advancing Time Series Forecasting via Hierarchical Text-Free Alignment with Large Language Models—0
TextToon: Real-Time Text Toonify Head Avatar from Single Video—0
Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-TrainingCode0
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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