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 5926–5950 of 6661 papers

TitleStatusHype
On the Adversarial Robustness of Graph Contrastive Learning Methods—0
On the Comparison between Multi-modal and Single-modal Contrastive Learning—0
On the Difficulty of Defending Contrastive Learning against Backdoor Attacks—0
On the Effectiveness of Sampled Softmax Loss for Item Recommendation—0
On the Effect of Data-Augmentation on Local Embedding Properties in the Contrastive Learning of Music Audio Representations—0
On the Importance of Contrastive Loss in Multimodal Learning—0
On the Informativeness of Supervision Signals—0
On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake?—0
On the Memorization Properties of Contrastive Learning—0
On the Provable Advantage of Unsupervised Pretraining—0
On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training—0
On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy—0
On the Sequence Evaluation based on Stochastic Processes—0
On the Sweet Spot of Contrastive Views for Knowledge-enhanced Recommendation—0
A Generalization Theory of Cross-Modality Distillation with Contrastive Learning—0
On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification—0
OOD Aware Supervised Contrastive Learning—0
OPEn: An Open-ended Physics Environment for Learning Without a Task—0
Open-Set Multivariate Time-Series Anomaly Detection—0
Open-Set Object Detection Using Classification-free Object Proposal and Instance-level Contrastive Learning—0
OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution—0
Open-Vocabulary 3D Detection via Image-level Class and Debiased Cross-modal Contrastive Learning—0
Open-Vocabulary Object Detection with Meta Prompt Representation and Instance Contrastive Optimization—0
Open-World Test-Time Training: Self-Training with Contrast Learning—0
Optimal Sample Complexity of Contrastive 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