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 1876–1900 of 6661 papers

TitleStatusHype
On Contrastive Learning for Likelihood-free InferenceCode1
CURL: Contrastive Unsupervised Representation Learning for Reinforcement LearningCode1
Understanding Contrastive Representation Learning through Geometry on the HypersphereCode1
Self-Supervised Learning of Pretext-Invariant RepresentationsCode1
Self-labelling via simultaneous clustering and representation learningCode1
Contrastive Representation DistillationCode1
Large Scale Adversarial Representation LearningCode1
Contrastive Multiview CodingCode1
UniVSE: Robust Visual Semantic Embeddings via Structured Semantic RepresentationsCode1
Unsupervised Feature Learning via Non-Parametric Instance-level DiscriminationCode1
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICACode1
SemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts—0
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation—0
Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management—0
HapticCap: A Multimodal Dataset and Task for Understanding User Experience of Vibration Haptic Signals—0
Similarity-Guided Diffusion for Contrastive Sequential Recommendation—0
LLM-Driven Dual-Level Multi-Interest Modeling for Recommendation—0
Latent Space Consistency for Sparse-View CT Reconstruction—0
Self-supervised pretraining of vision transformers for animal behavioral analysis and neural encoding—0
When Graph Contrastive Learning Backfires: Spectral Vulnerability and Defense in Recommendation—0
Pun Intended: Multi-Agent Translation of Wordplay with Contrastive Learning and Phonetic-Semantic Embeddings—0
Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations—0
CultureCLIP: Empowering CLIP with Cultural Awareness through Synthetic Images and Contextualized CaptionsCode0
From ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering RecommendationCode0
DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation—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