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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3851–3875 of 6661 papers

TitleStatusHype
Automatic Alignment of Discourse Relations of Different Discourse Annotation Frameworks—0
Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space—0
Auto-MLM: Improved Contrastive Learning for Self-supervised Multi-lingual Knowledge Retrieval—0
Auto-view contrastive learning for few-shot image recognition—0
AVATAR: Robust Voice Search Engine Leveraging Autoregressive Document Retrieval and Contrastive Learning—0
A vector quantized masked autoencoder for audiovisual speech emotion recognition—0
AVFF: Audio-Visual Feature Fusion for Video Deepfake Detection—0
A Visual Analytics Framework for Contrastive Network Analysis—0
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction—0
AVT: Audio-Video Transformer for Multimodal Action Recognition—0
AWEncoder: Adversarial Watermarking Pre-trained Encoders in Contrastive Learning—0
Backdoor Attacks in the Supply Chain of Masked Image Modeling—0
BaCon: Boosting Imbalanced Semi-supervised Learning via Balanced Feature-Level Contrastive Learning—0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
Balanced Adversarial Training: Balancing Tradeoffs Between Oversensitivity and Undersensitivity in NLP Models—0
Balanced Gradient Sample Retrieval for Enhanced Knowledge Retention in Proxy-based Continual Learning—0
Balanced Supervised Contrastive Learning for Few-Shot Class-Incremental Learning—0
Balancing Continual Learning and Fine-tuning for Human Activity Recognition—0
Balancing Robustness and Sensitivity using Feature Contrastive Learning—0
Banyan: Improved Representation Learning with Explicit Structure—0
Bayesian Distributional Policy Gradients—0
Bayesian Graph Contrastive Learning—0
BC-GAN: A Generative Adversarial Network for Synthesizing a Batch of Collocated Clothing—0
BDetCLIP: Multimodal Prompting Contrastive Test-Time Backdoor Detection—0
BeatDance: A Beat-Based Model-Agnostic Contrastive Learning Framework for Music-Dance Retrieval—0
Show:102550
← PrevPage 155 of 267Next →
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