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 5351–5375 of 6661 papers

TitleStatusHype
JEAN: Joint Expression and Audio-guided NeRF-based Talking Face Generation—0
JEMA: A Joint Embedding Framework for Scalable Co-Learning with Multimodal Alignment—0
jina-clip-v2: Multilingual Multimodal Embeddings for Text and Images—0
Jitter Does Matter: Adapting Gaze Estimation to New Domains—0
Joint Audio-Visual Attention with Contrastive Learning for More General Deepfake Detection—0
Joint enhancement of automatic chest X-ray diagnosis and radiological gaze prediction with multi-stage cooperative learning—0
Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition—0
Joint Data and Feature Augmentation for Self-Supervised Representation Learning on Point Clouds—0
Joint Debiased Representation and Image Clustering Learning with Self-Supervision—0
Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis—0
Joint End-to-End Image Compression and Denoising: Leveraging Contrastive Learning and Multi-Scale Self-ONNs—0
Joint Generative-Contrastive Representation Learning for Anomalous Sound Detection—0
Joint Learning of Context and Feedback Embeddings in Spoken Dialogue—0
Joint Low-level and High-level Textual Representation Learning with Multiple Masking Strategies—0
Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning—0
Joint Representation Learning and Novel Category Discovery on Single- and Multi-modal Data—0
Joint Salient Object Detection and Camouflaged Object Detection via Uncertainty-aware Learning—0
Joint Self-Supervised and Supervised Contrastive Learning for Multimodal MRI Data: Towards Predicting Abnormal Neurodevelopment—0
Joint semi-supervised and contrastive learning enables domain generalization and multi-domain segmentation—0
Joint Spatial-Temporal Modeling and Contrastive Learning for Self-supervised Heart Rate Measurement—0
Jo-SRC: A Contrastive Approach for Combating Noisy Labels—0
Judge Like a Real Doctor: Dual Teacher Sample Consistency Framework for Semi-supervised Medical Image Classification—0
Just a Hint: Point-Supervised Camouflaged Object Detection—0
Just-in-Time Detection of Silent Security Patches—0
KAAE: Numerical Reasoning for Knowledge Graphs via Knowledge-aware Attributes Learning—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