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 4076–4100 of 6661 papers

TitleStatusHype
Learning Contrastive Self-Distillation for Ultra-Fine-Grained Visual Categorization Targeting Limited Samples—0
ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation—0
TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree TransformationCode0
Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking—0
Hard-Negative Sampling for Contrastive Learning: Optimal Representation Geometry and Neural- vs Dimensional-CollapseCode0
Towards a Unified Framework of Contrastive Learning for Disentangled Representations—0
Learning Discriminative Features for Crowd Counting—0
Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks—0
CATE Estimation With Potential Outcome Imputation From Local Regression—0
SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation—0
Sparse Contrastive Learning of Sentence Embeddings—0
Temporal Graph Representation Learning with Adaptive Augmentation Contrastive—0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
Unifying Structure and Language Semantic for Efficient Contrastive Knowledge Graph Completion with Structured Entity Anchors—0
Contrastive Multi-Level Graph Neural Networks for Session-based Recommendation—0
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive LearningCode0
CycleCL: Self-supervised Learning for Periodic Videos—0
Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis—0
CheX-Nomaly: Segmenting Lung Abnormalities from Chest Radiographs using Machine Learning—0
SMORE: Score Models for Offline Goal-Conditioned Reinforcement Learning—0
FLAP: Fast Language-Audio Pre-training—0
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning—0
VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification—0
Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image ClassificationCode0
Learning Intra and Inter-Camera Invariance for Isolated Camera Supervised Person Re-identificationCode0
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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