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 5151–5200 of 6661 papers

TitleStatusHype
Combining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language—0
ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation—0
On Negative Sampling for Contrastive Audio-Text Retrieval—0
Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data—0
Enhanced Low-resolution LiDAR-Camera Calibration Via Depth Interpolation and Supervised Contrastive Learning—0
Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive LearningCode0
ConsPrompt: Exploiting Contrastive Samples for Fewshot Prompt Learning—0
Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness PredictionsCode0
CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human Presence Detection using Wi-Fi CSI—0
Camera Alignment and Weighted Contrastive Learning for Domain Adaptation in Video Person ReIDCode0
Contrastive Classification and Representation Learning with Probabilistic Interpretation—0
Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence EmbeddingCode0
Contrastive Learning enhanced Author-Style Headline GenerationCode0
CLOP: Video-and-Language Pre-Training with Knowledge Regularizations—0
Graph Contrastive Learning with Implicit AugmentationsCode0
Contrastive Weighted Learning for Near-Infrared Gaze Estimation—0
ProtoX: Explaining a Reinforcement Learning Agent via PrototypingCode0
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection—0
Contrastive Learning for Diverse Disentangled Foreground Generation—0
Latent Prompt Tuning for Text Summarization—0
MarginNCE: Robust Sound Localization with a Negative Margin—0
Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization—0
Query-based Instance Discrimination Network for Relational Triple Extraction—0
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning—0
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantificationCode0
On the Informativeness of Supervision Signals—0
Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity—0
Joint Data and Feature Augmentation for Self-Supervised Representation Learning on Point Clouds—0
Oracle-guided Contrastive Clustering—0
Building an Enhanced Autoregressive Document Retriever Leveraging Supervised Contrastive Learning—0
Invariant and consistent: Unsupervised representation learning for few-shot visual recognition—0
SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking—0
Adaptive Speech Quality Aware Complex Neural Network for Acoustic Echo Cancellation with Supervised Contrastive Learning—0
Image-free Domain Generalization via CLIP for 3D Hand Pose Estimation—0
Rare Wildlife Recognition with Self-Supervised Representation LearningCode0
Pair DETR: Contrastive Learning Speeds Up DETR Training—0
Discriminative Speaker Representation via Contrastive Learning with Class-Aware Attention in Angular Space—0
Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis—0
Conversation Disentanglement with Bi-Level Contrastive Learning—0
Dictionary-Assisted Supervised Contrastive LearningCode0
Multi-view Contrastive Learning with Additive Margin for Adaptive Nasopharyngeal Carcinoma Radiotherapy Prediction—0
Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings—0
Self-Supervised Training of Speaker Encoder with Multi-Modal Diverse Positive Pairs—0
Learning Joint Representation of Human Motion and Language—0
Bi-Link: Bridging Inductive Link Predictions from Text via Contrastive Learning of Transformers and Prompts—0
Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis—0
Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs—0
Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing—0
IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image SegmentationCode0
A Chinese Spelling Check Framework Based on Reverse 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