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 5351–5375 of 6661 papers

TitleStatusHype
Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing—0
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning—0
Fair Node Representation Learning via Adaptive Data Augmentation—0
Dual Contrastive Learning: Text Classification via Label-Aware Data AugmentationCode1
Contrastive and Selective Hidden Embeddings for Medical Image SegmentationCode0
Self-supervised Video Representation Learning with Cascade Positive RetrievalCode0
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagationCode1
Watermarking Pre-trained Encoders in Contrastive Learning—0
Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image EncodersCode1
TriCoLo: Trimodal Contrastive Loss for Text to Shape Retrieval—0
Dual Space Graph Contrastive Learning—0
Weakly Supervised Contrastive Learning for Better Severity Scoring of Lung UltrasoundCode1
MuSCLe: A Multi-Strategy Contrastive Learning Framework for Weakly Supervised Semantic Segmentation—0
RePre: Improving Self-Supervised Vision Transformer with Reconstructive Pre-training—0
The token parser and manipulator, next-generation Deep Learning architecture—0
Prototypical Representation Learning for Low-resource Knowledge Extraction: Summary and Perspective—0
Towards Unsupervised Deep Graph Structure LearningCode1
On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification—0
Interactive Contrastive Learning for Self-supervised Entity Alignment—0
Balanced Adversarial Training: Balancing Tradeoffs Between Oversensitivity and Undersensitivity in NLP Models—0
EASE: Entity-Aware Contrastive Learning of Sentence Embedding—0
Deep Continuous Prompt for Contrastive Learning of Sentence Embeddings—0
Probing the Role of Positional Information in Vision-Language Models—0
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning—0
Focus-Driven Contrastive Learning for Medical Question Summarization—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