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 2151–2175 of 6661 papers

TitleStatusHype
Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation DistillationCode1
Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift—0
LoDisc: Learning Global-Local Discriminative Features for Self-Supervised Fine-Grained Visual Recognition—0
Cascaded Self-supervised Learning for Subject-independent EEG-based Emotion Recognition—0
Inference via Interpolation: Contrastive Representations Provably Enable Planning and InferenceCode1
Intent-aware Recommendation via Disentangled Graph Contrastive Learning—0
Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation—0
A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation—0
Contrastive Learning of Person-independent Representations for Facial Action Unit Detection—0
Multi-Grained Cross-modal Alignment for Learning Open-vocabulary Semantic Segmentation from Text Supervision—0
DP-CRE: Continual Relation Extraction via Decoupled Contrastive Learning and Memory Structure Preservation—0
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment—0
Contrastive Pre-training for Deep Session Data Understanding—0
Dual Mean-Teacher: An Unbiased Semi-Supervised Framework for Audio-Visual Source LocalizationCode1
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive ModelsCode0
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal DecouplingCode1
Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives—0
Enhancing Conceptual Understanding in Multimodal Contrastive Learning through Hard Negative Samples—0
Multi-Scale Subgraph Contrastive Learning—0
A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive LearningCode1
BaCon: Boosting Imbalanced Semi-supervised Learning via Balanced Feature-Level Contrastive Learning—0
Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion—0
A Theoretical Analysis of Self-Supervised Learning for Vision Transformers—0
NoteLLM: A Retrievable Large Language Model for Note Recommendation—0
Addressing Long-Tail Noisy Label Learning Problems: a Two-Stage Solution with Label Refurbishment Considering Label Rarity—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