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 4301–4325 of 6661 papers

TitleStatusHype
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule DiagnosisCode1
Pretraining Respiratory Sound Representations using Metadata and Contrastive LearningCode1
Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings—0
Robust Data2vec: Noise-robust Speech Representation Learning for ASR by Combining Regression and Improved Contrastive LearningCode1
Multi-view Contrastive Learning with Additive Margin for Adaptive Nasopharyngeal Carcinoma Radiotherapy Prediction—0
Learning Joint Representation of Human Motion and Language—0
Conversation Disentanglement with Bi-Level Contrastive Learning—0
Dictionary-Assisted Supervised Contrastive LearningCode0
Self-Supervised Training of Speaker Encoder with Multi-Modal Diverse Positive Pairs—0
MABEL: Attenuating Gender Bias using Textual Entailment DataCode1
Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing—0
Bi-Link: Bridging Inductive Link Predictions from Text via Contrastive Learning of Transformers and Prompts—0
IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image SegmentationCode0
Broken Neural Scaling LawsCode1
Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs—0
Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis—0
Boosting Semi-Supervised Semantic Segmentation with Probabilistic RepresentationsCode1
IMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and TextCode1
Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion with Missing Data—0
Line Graph Contrastive Learning for Link PredictionCode0
Contrastive Search Is What You Need For Neural Text GenerationCode2
Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical EncodingsCode1
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation—0
A Chinese Spelling Check Framework Based on Reverse Contrastive Learning—0
Event-Centric Question Answering via Contrastive Learning and Invertible Event TransformationCode0
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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