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
Robust Cross-View Geo-Localization via Content-Viewpoint Disentanglement—0
Robust Federated Contrastive Recommender System against Model Poisoning Attack—0
Robust Federated Learning on Edge Devices with Domain Heterogeneity—0
Robust Fraud Detection via Supervised Contrastive Learning—0
Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures—0
Robust Mean Teacher for Continual and Gradual Test-Time Adaptation—0
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning—0
Robust Representation Learning for Unreliable Partial Label Learning—0
Robust Single Object Tracking in LiDAR Point Clouds under Adverse Weather Conditions—0
Table Integration in Data Lakes Unleashed: Pairwise Integrability Judgment, Integrable Set Discovery, and Multi-Tuple Conflict Resolution—0
Robust Task-Oriented Dialogue Generation with Contrastive Pre-training and Adversarial Filtering—0
RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining—0
ROLL: Robust Noisy Pseudo-label Learning for Multi-View Clustering with Noisy Correspondence—0
RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations—0
Rotation-Adaptive Point Cloud Domain Generalization via Intricate Orientation Learning—0
RSAM: Learning on manifolds with Riemannian Sharpness-aware Minimization—0
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation—0
Run Away From your Teacher: Understanding BYOL by a Novel Self-Supervised Approach—0
RUSH: Robust Contrastive Learning via Randomized Smoothing—0
S2RC-GCN: A Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Images—0
S^3ADNet: Sequential Anomaly Detection with Pessimistic Contrastive Learning—0
S^3R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification—0
SAICL: Student Modelling with Interaction-level Auxiliary Contrastive Tasks for Knowledge Tracing and Dropout Prediction—0
SALAD: Improving Robustness and Generalization through Contrastive Learning with Structure-Aware and LLM-Driven Augmented Data—0
Saliency Guided Contrastive Learning on Scene Images—0
SaliencyI2PLoc: saliency-guided image-point cloud localization using contrastive learning—0
SAMa: Material-aware 3D Selection and Segmentation—0
SAMCLR: Contrastive pre-training on complex scenes using SAM for view sampling—0
SampleMatch: Drum Sample Retrieval by Musical Context—0
Sample-Specific Debiasing for Better Image-Text Models—0
SamToNe: Improving Contrastive Loss for Dual Encoder Retrieval Models with Same Tower Negatives—0
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning—0
Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images—0
Sat2Sound: A Unified Framework for Zero-Shot Soundscape Mapping—0
SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensing—0
Scalable Deep Metric Learning on Attributed Graphs—0
SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks—0
SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition—0
Scale Contrastive Learning with Selective Attentions for Blind Image Quality Assessment—0
Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation—0
Scaling Law for Recommendation Models: Towards General-purpose User Representations—0
Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization—0
SCALP -- Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata—0
SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption—0
SCAT: Robust Self-supervised Contrastive Learning via Adversarial Training for Text Classification—0
SCDNet: Self-supervised Learning Feature-based Speaker Change Detection—0
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition—0
Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation—0
Scene Graph Contrastive Learning for Embodied Navigation—0
Scene Graph Embeddings Using Relative Similarity Supervision—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