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 2551–2575 of 6661 papers

TitleStatusHype
CorMulT: A Semi-supervised Modality Correlation-aware Multimodal Transformer for Sentiment Analysis—0
BGM2Pose: Active 3D Human Pose Estimation with Non-Stationary Sounds—0
Fusion Self-supervised Learning for Recommendation—0
CORI: CJKV Benchmark with Romanization Integration -- A step towards Cross-lingual Transfer Beyond Textual Scripts—0
CoReGAN: Contrastive Regularized Generative Adversarial Network for Guided Depth Map Super Resolution—0
Rolling with the Punches: Resilient Contrastive Pre-training under Non-Stationary Drift—0
CoReFace: Sample-Guided Contrastive Regularization for Deep Face Recognition—0
Causal Contrastive Learning for Counterfactual Regression Over Time—0
A Two-Stage Progressive Pre-training using Multi-Modal Contrastive Masked Autoencoders—0
Histopathology Image Classification using Deep Manifold Contrastive Learning—0
How to Make Cross Encoder a Good Teacher for Efficient Image-Text Retrieval?—0
ID-MixGCL: Identity Mixup for Graph Contrastive Learning—0
CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data—0
Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture—0
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning—0
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations—0
Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection—0
ConViTac: Aligning Visual-Tactile Fusion with Contrastive Representations—0
CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection—0
ConVerSum: A Contrastive Learning-based Approach for Data-Scarce Solution of Cross-Lingual Summarization Beyond Direct Equivalents—0
Adversarial Masking Contrastive Learning for vein recognition—0
Hierarchical Self-supervised Representation Learning for Movie Understanding—0
Conversation Disentanglement with Bi-Level Contrastive Learning—0
Convergence of energy-based learning in linear resistive networks—0
CAST: Corpus-Aware Self-similarity Enhanced Topic modelling—0
Show:102550
← PrevPage 103 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