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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2401–2425 of 6661 papers

TitleStatusHype
Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization—0
Contrastive Learning With Audio Discrimination For Customizable Keyword Spotting In Continuous Speech—0
TransliCo: A Contrastive Learning Framework to Address the Script Barrier in Multilingual Pretrained Language ModelsCode0
CCFC: Bridging Federated Clustering and Contrastive LearningCode0
HiCMAE: Hierarchical Contrastive Masked Autoencoder for Self-Supervised Audio-Visual Emotion RecognitionCode2
Learn From Zoom: Decoupled Supervised Contrastive Learning For WCE Image ClassificationCode2
Mitigating Unhelpfulness in Emotional Support Conversations with Multifaceted AI FeedbackCode1
Secrets of RLHF in Large Language Models Part II: Reward ModelingCode5
Enhancing Contrastive Learning with Efficient Combinatorial Positive Pairing—0
End-to-end Learnable Clustering for Intent Learning in RecommendationCode2
ConKeD: Multiview contrastive descriptor learning for keypoint-based retinal image registration—0
Self-supervised speech representation and contextual text embedding for match-mismatch classification with EEG recordingCode1
Relaxed Contrastive Learning for Federated LearningCode1
SwiMDiff: Scene-wide Matching Contrastive Learning with Diffusion Constraint for Remote Sensing Image—0
BELHD: Improving Biomedical Entity Linking with Homonoym Disambiguation—0
ECC-PolypDet: Enhanced CenterNet with Contrastive Learning for Automatic Polyp DetectionCode0
Improving the Robustness of Knowledge-Grounded Dialogue via Contrastive LearningCode0
Connect Later: Improving Fine-tuning for Robustness with Targeted AugmentationsCode0
Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking—0
Two-stream joint matching method based on contrastive learning for few-shot action recognition—0
Efficient Selective Audio Masked Multimodal Bottleneck Transformer for Audio-Video Classification—0
Unifying Graph Contrastive Learning via Graph Message Augmentation—0
End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction—0
Automated Detection of Myopic Maculopathy in MMAC 2023: Achievements in Classification, Segmentation, and Spherical Equivalent PredictionCode0
Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech RepresentationCode0
Show:102550
← PrevPage 97 of 267Next →
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