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 2751–2775 of 6661 papers

TitleStatusHype
A Classifier-Free Incremental Learning Framework for Scalable Medical Image Segmentation—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
Contrastive Learning Relies More on Spatial Inductive Bias Than Supervised Learning: An Empirical Study—0
An Iterative Classification and Semantic Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images—0
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations—0
Goal-conditioned reinforcement learning for ultrasound navigation guidance—0
GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks—0
Contrastive Learning on Multimodal Analysis of Electronic Health Records—0
Breast tumor classification based on self-supervised contrastive learning from ultrasound videos—0
3D-CLFusion: Fast Text-to-3D Rendering with Contrastive Latent Diffusion—0
Contrastive Learning on Medical Intents for Sequential Prescription Recommendation—0
Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images—0
GMM-Based Comprehensive Feature Extraction and Relative Distance Preservation For Few-Shot Cross-Modal Retrieval—0
Contrastive Learning of Visual-Semantic Embeddings—0
Advancing Multi-Party Dialogue Framework with Speaker-ware Contrastive Learning—0
GrabDAE: An Innovative Framework for Unsupervised Domain Adaptation Utilizing Grab-Mask and Denoise Auto-Encoder—0
Global Contrastive Training for Multimodal Electronic Health Records with Language Supervision—0
Contrastive Learning of View-Invariant Representations for Facial Expressions Recognition—0
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning—0
Gradient Regularized Contrastive Learning for Continual Domain Adaptation—0
Gradient Regularized Contrastive Learning for Continual Domain Adaptation—0
Breaking the Global North Stereotype: A Global South-centric Benchmark Dataset for Auditing and Mitigating Biases in Facial Recognition Systems—0
Contrastive Learning to Improve Retrieval for Real-world Fact Checking—0
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach—0
Improving Contrastive Learning on Visually Homogeneous Mars Rover Images—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