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 4451–4475 of 6661 papers

TitleStatusHype
Contrastive Audio-Visual Masked AutoencoderCode2
Fine-grained Contrastive Learning for Definition GenerationCode0
Spectral Augmentation for Self-Supervised Learning on GraphsCode1
Reducing Spurious Correlations for Answer Selection by Feature Decorrelation and Language DebiasingCode0
Improving Deep Embedded Clustering via Learning Cluster-level Representations—0
Generalizable Implicit Hate Speech Detection Using Contrastive LearningCode1
Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis—0
DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction—0
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity RecognitionCode1
Table-based Fact Verification with Self-labeled Keypoint Alignment—0
CoCGAN: Contrastive Learning for Adversarial Category Text Generation—0
Focus-Driven Contrastive Learning for Medical Question Summarization—0
Domain Generalization for Text Classification with Memory-Based Supervised Contrastive LearningCode0
Automated Essay Scoring via Pairwise Contrastive RegressionCode1
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases—0
Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance DetectionCode0
E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models—0
Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning—0
Don’t Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention PoolingCode0
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis—0
CLoSE: Contrastive Learning of Subframe Embeddings for Political Bias Classification of News MediaCode0
ConIsI: A Contrastive Framework with Inter-sentence Interaction for Self-supervised Sentence Representation—0
Abstains from Prediction: Towards Robust Relation Extraction in Real World—0
Supervised Contrastive Learning for Cross-lingual Transfer Learning—0
Improving Event Temporal Relation Classification via Auxiliary Label-Aware Contrastive Learning—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