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 4976–5000 of 6661 papers

TitleStatusHype
Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes—0
Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing—0
G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory—0
ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness—0
Gaga: Group Any Gaussians via 3D-aware Memory Bank—0
GAIR: Improving Multimodal Geo-Foundation Model with Geo-Aligned Implicit Representations—0
Game and Reference: Policy Combination Synthesis for Epidemic Prevention and Control—0
Game State Learning via Game Scene Augmentation—0
GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning—0
GANORCON: Are Generative Models Useful for Few-shot Segmentation?—0
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning—0
GarmentAligner: Text-to-Garment Generation via Retrieval-augmented Multi-level Corrections—0
Gated Multimodal Fusion with Contrastive Learning for Turn-taking Prediction in Human-robot Dialogue—0
Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation—0
Gaze Estimation with Eye Region Segmentation and Self-Supervised Multistream Learning—0
GCC: Generative Calibration Clustering—0
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels—0
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition—0
GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors—0
GenCAD-Self-Repairing: Feasibility Enhancement for 3D CAD Generation—0
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing—0
CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy—0
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning—0
Generalization Analysis for Contrastive Representation Learning—0
Generalization Analysis for Contrastive Representation Learning under Non-IID Settings—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