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 6151–6175 of 6661 papers

TitleStatusHype
Rethinking Temperature in Graph Contrastive LearningCode0
Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching—0
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning—0
Contrastive Quant: Quantization Makes Stronger Contrastive Learning—0
AAVAE: Augmentation-Augmented Variational Autoencoders—0
Contrastive Pre-training for Zero-Shot Information Retrieval—0
What Makes for Good Representations for Contrastive Learning—0
Fine-grained Software Vulnerability Detection via Information Theory and Contrastive Learning—0
Not All Regions are Worthy to be Distilled: Region-aware Knowledge Distillation Towards Efficient Image-to-Image Translation—0
Contrastive Mutual Information Maximization for Binary Neural Networks—0
f-Mutual Information Contrastive Learning—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—0
S^3ADNet: Sequential Anomaly Detection with Pessimistic Contrastive Learning—0
Chaos is a Ladder: A New Understanding of Contrastive Learning—0
Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction PredictionCode0
The JHU submission to VoxSRC-21: Track 3—0
A Contrastive Learning Approach to Auroral Identification and Classification—0
Modelling Neighbor Relation in Joint Space-Time Graph for Video Correspondence Learning—0
Dynamic Modeling of Hand-Object Interactions via Tactile Sensing.—0
Click-through Rate Prediction with Auto-Quantized Contrastive Learning—0
Cluster Analysis with Deep Embeddings and Contrastive Learning—0
Self-Supervised Video Representation Learning by Video Incoherence Detection—0
Contrastive Learning for Mitochondria Segmentation—0
Dense Contrastive Visual-Linguistic Pretraining—0
Long Short View Feature Decomposition via Contrastive Video Representation 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