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 3076–3100 of 6661 papers

TitleStatusHype
A dual contrastive framework—0
Gaze Estimation with Eye Region Segmentation and Self-Supervised Multistream Learning—0
iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition—0
Contrastive Learning Guided Latent Diffusion Model for Image-to-Image Translation—0
Contrastive Learning from Synthetic Audio Doppelgängers—0
CorMulT: A Semi-supervised Modality Correlation-aware Multimodal Transformer for Sentiment Analysis—0
BGM2Pose: Active 3D Human Pose Estimation with Non-Stationary Sounds—0
Bootstrapping Contrastive Learning Enhanced Music Cold-Start Matching—0
Multimodal Fusion and Coherence Modeling for Video Topic Segmentation—0
Improved baselines for vision-language pre-training—0
Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation—0
Gated Multimodal Fusion with Contrastive Learning for Turn-taking Prediction in Human-robot Dialogue—0
Contrastive Learning from Pairwise Measurements—0
Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models—0
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning—0
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System—0
GarmentAligner: Text-to-Garment Generation via Retrieval-augmented Multi-level Corrections—0
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning—0
Improved Forward-Forward Contrastive Learning—0
GANORCON: Are Generative Models Useful for Few-shot Segmentation?—0
Improve Unsupervised Pretraining for Few-label Transfer—0
GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning—0
Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning—0
Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment—0
Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation—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