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 1676–1700 of 6661 papers

TitleStatusHype
Modeling Comparative Logical Relation with Contrastive Learning for Text Generation—0
An Efficient Post-hoc Framework for Reducing Task Discrepancy of Text Encoders for Composed Image RetrievalCode2
Federated Contrastive Learning for Personalized Semantic Communication—0
CLDTA: Contrastive Learning based on Diagonal Transformer Autoencoder for Cross-Dataset EEG Emotion Recognition—0
SCDNet: Self-supervised Learning Feature-based Speaker Change Detection—0
Languages Transferred Within the Encoder: On Representation Transfer in Zero-Shot Multilingual TranslationCode0
Label-aware Hard Negative Sampling Strategies with Momentum Contrastive Learning for Implicit Hate Speech DetectionCode0
Exploring Self-Supervised Multi-view Contrastive Learning for Speech Emotion Recognition with Limited Annotations—0
Vision Model Pre-training on Interleaved Image-Text Data via Latent Compression LearningCode2
Training Dynamics of Nonlinear Contrastive Learning Model in the High Dimensional Limit—0
Can We Achieve High-quality Direct Speech-to-Speech Translation without Parallel Speech Data?—0
Joint Learning of Context and Feedback Embeddings in Spoken Dialogue—0
Learning Domain-Invariant Features for Out-of-Context News Detection—0
Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective—0
Benchmarking Vision-Language Contrastive Methods for Medical Representation LearningCode0
SSCL-IDS: Enhancing Generalization of Intrusion Detection with Self-Supervised Contrastive LearningCode0
Weighted KL-Divergence for Document Ranking Model Refinement—0
Contrastive learning of T cell receptor representationsCode0
NeuroMoCo: A Neuromorphic Momentum Contrast Learning Method for Spiking Neural Networks—0
Text-aware and Context-aware Expressive Audiobook Speech Synthesis—0
PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive LearningCode0
Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and LanguageCode2
Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World AttacksCode1
Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New TaskCode0
Deep Learning to Predict Glaucoma Progression using Structural Changes in the Eye—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