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

Showing 48014825 of 6661 papers

TitleStatusHype
VLCap: Vision-Language with Contrastive Learning for Coherent Video Paragraph CaptioningCode1
SLIC: Self-Supervised Learning with Iterative Clustering for Human Action VideosCode1
Self-supervised Context-aware Style Representation for Expressive Speech Synthesis0
Language Models as Knowledge EmbeddingsCode1
Geometry Contrastive Learning on Heterogeneous GraphsCode0
Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning0
Graph Component Contrastive Learning for Concept Relatedness EstimationCode0
Contrastive Learning of Features between Images and LiDAR0
Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training0
CoSP: Co-supervised pretraining of pocket and ligand0
Towards Galaxy Foundation Models with Hybrid Contrastive LearningCode1
Utilizing Expert Features for Contrastive Learning of Time-Series RepresentationsCode1
CLAMP: Prompt-based Contrastive Learning for Connecting Language and Animal PoseCode1
A Feature Memory Rearrangement Network for Visual Inspection of Textured Surface Defects Toward Edge Intelligent Manufacturing0
Panoramic Panoptic Segmentation: Insights Into Surrounding Parsing for Mobile Agents via Unsupervised Contrastive LearningCode1
Probing Visual-Audio Representation for Video Highlight Detection via Hard-Pairs Guided Contrastive Learning0
Enhancing Multi-view Stereo with Contrastive Matching and Weighted Focal LossCode2
Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive LearningCode1
Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation LearningCode0
Rethinking Audio-visual Synchronization for Active Speaker Detection0
Visualizing and Understanding Contrastive LearningCode0
Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval0
C-SENN: Contrastive Self-Explaining Neural Network0
Conditioned and Composed Image Retrieval Combining and Partially Fine-Tuning CLIP-Based FeaturesCode1
Self-Supervised Learning for Videos: A SurveyCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6Unverified
2ResNet50ImageNet Top-1 Accuracy73Unverified
3ResNet50ImageNet Top-1 Accuracy71.1Unverified
4ResNet50ImageNet Top-1 Accuracy69.3Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8Unverified
7ResNet50ImageNet Top-1 Accuracy63.6Unverified
8ResNet50ImageNet Top-1 Accuracy61.5Unverified
9ResNet50ImageNet Top-1 Accuracy61.5Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55Unverified