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 6051–6075 of 6661 papers

TitleStatusHype
Give the Truth: Incorporate Semantic Slot into Abstractive Dialogue Summarization—0
Grammatical Error Correction with Contrastive Learning in Low Error Density DomainsCode0
KFCNet: Knowledge Filtering and Contrastive Learning for Generative Commonsense Reasoning—0
Counter-Contrastive Learning for Language GANs—0
Exploring Non-Autoregressive Text Style TransferCode0
Semi-supervised Intent Discovery with Contrastive Learning—0
Dialogue Response Generation via Contrastive Latent Representation Learning—0
TransAug: Translate as Augmentation for Sentence Embeddings—0
Explicitly Modeling the Discriminability for Instance-Aware Visual Object Tracking—0
Graph Communal Contrastive LearningCode0
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction—0
InfoGCL: Information-Aware Graph Contrastive Learning—0
Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing—0
RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classification—0
NIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks—0
SCALP -- Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata—0
GenURL: A General Framework for Unsupervised Representation Learning—0
Deeper-GXX: Deepening Arbitrary GNNs—0
Towards More Generalizable One-shot Visual Imitation Learning—0
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling—0
CLLD: Contrastive Learning with Label Distance for Text Classification—0
No One Representation to Rule Them All: Overlapping Features of Training Methods—0
Contrastive Document Representation Learning with Graph Attention Networks—0
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling—0
VLDeformer: Vision-Language Decomposed Transformer for Fast Cross-Modal Retrieval—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