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 4451–4475 of 6661 papers

TitleStatusHype
CvFormer: Cross-view transFormers with Pre-training for fMRI Analysis of Human Brain—0
CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset—0
CycleCL: Self-supervised Learning for Periodic Videos—0
Cycle-Contrast for Self-Supervised Video Representation Learning—0
Cycle Contrastive Adversarial Learning for Unsupervised image Deraining—0
D2CSE: Difference-aware Deep continuous prompts for Contrastive Sentence Embeddings—0
Introducing Depth into Transformer-based 3D Object Detection—0
DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection—0
Damage GAN: A Generative Model for Imbalanced Data—0
DA-RAW: Domain Adaptive Object Detection for Real-World Adverse Weather Conditions—0
DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System—0
DarkFarseer: Inductive Spatio-temporal Kriging via Hidden Style Enhancement and Sparsity-Noise Mitigation—0
DART: Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation—0
DARTS: A Dual-View Attack Framework for Targeted Manipulation in Federated Sequential Recommendation—0
DashCLIP: Leveraging multimodal models for generating semantic embeddings for DoorDash—0
Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification—0
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition—0
Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise—0
Data curation via joint example selection further accelerates multimodal learning—0
Data-Efficient Contrastive Learning by Differentiable Hard Sample and Hard Positive Pair Generation—0
DATA: Multi-Disentanglement based Contrastive Learning for Open-World Semi-Supervised Deepfake Attribution—0
DAug: Diffusion-based Channel Augmentation for Radiology Image Retrieval and Classification—0
DB-GNN: Dual-Branch Graph Neural Network with Multi-Level Contrastive Learning for Jointly Identifying Within- and Cross-Frequency Coupled Brain Networks—0
Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation—0
DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction—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