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 2026–2050 of 6661 papers

TitleStatusHype
Automatic Alignment of Discourse Relations of Different Discourse Annotation Frameworks—0
FewUser: Few-Shot Social User Geolocation via Contrastive Learning—0
Developing Healthcare Language Model Embedding Spaces—0
PoCo: A Self-Supervised Approach via Polar Transformation Based Progressive Contrastive Learning for Ophthalmic Disease DiagnosisCode0
Siamese Vision Transformers are Scalable Audio-visual LearnersCode1
The Bad Batches: Enhancing Self-Supervised Learning in Image Classification Through Representative Batch Curation—0
Mixed Preference Optimization: Reinforcement Learning with Data Selection and Better Reference Model—0
PointCloud-Text Matching: Benchmark Datasets and a Baseline—0
Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Mean-Variance Efficient Sampling—0
Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users—0
Deep Fusion: Capturing Dependencies in Contrastive Learning via Transformer Projection Heads—0
Towards Non-Exemplar Semi-Supervised Class-Incremental Learning—0
Preventing Collapse in Contrastive Learning with Orthonormal Prototypes (CLOP)—0
Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications—0
OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental LearningCode1
NJUST-KMG at TRAC-2024 Tasks 1 and 2: Offline Harm Potential Identification—0
HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text ClassificationCode1
KDMCSE: Knowledge Distillation Multimodal Sentence Embeddings with Adaptive Angular margin Contrastive LearningCode1
EulerFormer: Sequential User Behavior Modeling with Complex Vector AttentionCode1
To Supervise or Not to Supervise: Understanding and Addressing the Key Challenges of Point Cloud Transfer Learning—0
Joint enhancement of automatic chest X-ray diagnosis and radiological gaze prediction with multi-stage cooperative learning—0
DreamLIP: Language-Image Pre-training with Long CaptionsCode2
Cross-lingual Contextualized Phrase RetrievalCode0
CMViM: Contrastive Masked Vim Autoencoder for 3D Multi-modal Representation Learning for AD classification—0
Transfer Learning of Real Image Features with Soft Contrastive Loss for Fake Image Detection—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