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 3151–3175 of 6661 papers

TitleStatusHype
LAVA: Language Audio Vision Alignment for Contrastive Video Pre-Training—0
LEARNER: Learning Granular Labels from Coarse Labels using Contrastive Learning—0
Game State Learning via Game Scene Augmentation—0
Improving Pediatric Pneumonia Diagnosis with Adult Chest X-ray Images Utilizing Contrastive Learning and Embedding Similarity—0
Game and Reference: Policy Combination Synthesis for Epidemic Prevention and Control—0
Improving PTM Site Prediction by Coupling of Multi-Granularity Structure and Multi-Scale Sequence Representation—0
Contrastive Learning from Demonstrations—0
Improving Radiology Summarization with Radiograph and Anatomy Prompts—0
GAIR: Improving Multimodal Geo-Foundation Model with Geo-Aligned Implicit Representations—0
Contrastive Learning for View Classification of Echocardiograms—0
Gaga: Group Any Gaussians via 3D-aware Memory Bank—0
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning—0
ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness—0
Contrastive Learning for Unsupervised Video Highlight Detection—0
A New Brain Network Construction Paradigm for Brain Disorder via Diffusion-based Graph Contrastive Learning—0
Latent Prompt Tuning for Text Summarization—0
G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory—0
Contrastive Learning for Unsupervised Radar Place Recognition—0
CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human Presence Detection using Wi-Fi CSI—0
Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection—0
Bootstrap Equilibrium and Probabilistic Speaker Representation Learning for Self-supervised Speaker Verification—0
Contrastive learning for unsupervised medical image clustering and reconstruction—0
A dual-branch model with inter- and intra-branch contrastive loss for long-tailed recognition—0
Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing—0
Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes—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