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 4451–4500 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
DD-rPPGNet: De-interfering and Descriptive Feature Learning for Unsupervised rPPG Estimation—0
DebCSE: Rethinking Unsupervised Contrastive Sentence Embedding Learning in the Debiasing Perspective—0
Debias-CLR: A Contrastive Learning Based Debiasing Method for Algorithmic Fairness in Healthcare Applications—0
(Debiased) Contrastive Learning Loss for Recommendation (Technical Report)—0
Debiased Contrastive Learning of Unsupervised Sentence Representations—0
Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems—0
Debiased Model-based Interactive Recommendation—0
Debiased Novel Category Discovering and Localization—0
Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector—0
Debiasing Multimodal Sarcasm Detection with Contrastive Learning—0
Decentralized Collective World Model for Emergent Communication and Coordination—0
Decoding Realistic Images from Brain Activity with Contrastive Self-supervision and Latent Diffusion—0
Deconstruct Complexity (DeComplex): A Novel Perspective on Tackling Dense Action Detection—0
Decoupled Doubly Contrastive Learning for Cross Domain Facial Action Unit Detection—0
Decoupled Spatial Temporal Graphs for Generic Visual Grounding—0
DeDe: Detecting Backdoor Samples for SSL Encoders via Decoders—0
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision—0
Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions—0
Deep Augmentation: Self-Supervised Learning with Transformations in Activation Space—0
Deep Bregman Divergence for Contrastive Learning of Visual Representations—0
DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks—0
Deep Clustering by Semantic Contrastive Learning—0
Deep Code Search with Naming-Agnostic Contrastive Multi-View Learning—0
Deep Continuous Prompt for Contrastive Learning of Sentence Embeddings—0
Deep Contrastive Graph Learning with Clustering-Oriented Guidance—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