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 3226–3250 of 6661 papers

TitleStatusHype
Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning—0
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification—0
Distance-rank Aware Sequential Reward Learning for Inverse Reinforcement Learning with Sub-optimal Demonstrations—0
CO2: Consistent Contrast for Unsupervised Visual Representation Learning—0
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning—0
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels—0
CMViM: Contrastive Masked Vim Autoencoder for 3D Multi-modal Representation Learning for AD classification—0
Dissecting Representation Misalignment in Contrastive Learning via Influence Function—0
CMV-BERT: Contrastive multi-vocab pretraining of BERT—0
Disentangling Learnable and Memorizable Data via Contrastive Learning for Semantic Communications—0
CMSBERT-CLR: Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations—0
CMLM-CSE: Based on Conditional MLM Contrastive Learning for Sentence Embeddings—0
A Learnable Multi-views Contrastive Framework with Reconstruction Discrepancy for Medical Time-Series—0
ACTIVE:Augmentation-Free Graph Contrastive Learning for Partial Multi-View Clustering—0
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning—0
Feedback Reciprocal Graph Collaborative Filtering—0
Disentangle Perceptual Learning through Online Contrastive Learning—0
Disentangled Graph Contrastive Learning for Review-based Recommendation—0
cMIM: A Contrastive Mutual Information Framework for Unified Generative and Discriminative Representation Learning—0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
Disentangled Contrastive Learning on Graphs—0
Disentangled Contrastive Image Translation for Nighttime Surveillance—0
CMAL: A Novel Cross-Modal Associative Learning Framework for Vision-Language Pre-Training—0
Attention-wise masked graph contrastive learning for predicting molecular property—0
Discriminative Speaker Representation via Contrastive Learning with Class-Aware Attention in Angular Space—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