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 5251–5300 of 6661 papers

TitleStatusHype
Improving Node Representation by Boosting Target-Aware Contrastive Loss—0
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction—0
Improving Object Detection via Local-global Contrastive Learning—0
Improving Pediatric Pneumonia Diagnosis with Adult Chest X-ray Images Utilizing Contrastive Learning and Embedding Similarity—0
Improving Pixel-Level Contrastive Learning by Leveraging Exogenous Depth Information—0
Improving PTM Site Prediction by Coupling of Multi-Granularity Structure and Multi-Scale Sequence Representation—0
Improving Radiology Summarization with Radiograph and Anatomy Prompts—0
Improving Reference-based Distinctive Image Captioning with Contrastive Rewards—0
Improving Sound Source Localization with Joint Slot Attention on Image and Audio—0
Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning—0
Improving Tail-Class Representation with Centroid Contrastive Learning—0
Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis—0
Improving vision-language alignment with graph spiking hybrid Networks—0
Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions—0
SimCSE++: Improving Contrastive Learning for Sentence Embeddings from Two Perspectives—0
Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt—0
Inclusive FinTech Lending via Contrastive Learning and Domain Adaptation—0
Incomplete Multi-view Clustering via Diffusion Completion—0
Incomplete Multi-view Clustering via Diffusion Contrastive Generation—0
Incomplete Multi-view Multi-label Classification via a Dual-level Contrastive Learning Framework—0
Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning—0
In-Context Learning for Few-Shot Nested Named Entity Recognition—0
Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens—0
Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning—0
Incorporating Dense Knowledge Alignment into Unified Multimodal Representation Models—0
Incorporating granularity bias as the margin into contrastive loss for video captioning—0
Incremental False Negative Detection for Contrastive Learning—0
Indoor Smartphone SLAM with Learned Echoic Location Features—0
Inductive-Biases for Contrastive Learning of Disentangled Representations—0
INDUS: Effective and Efficient Language Models for Scientific Applications—0
Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning—0
InfoGCL: Information-Aware Graph Contrastive Learning—0
InfoNCE: Identifying the Gap Between Theory and Practice—0
Information-Aware Time Series Meta-Contrastive Learning—0
Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining—0
Information-guided pixel augmentation for pixel-wise contrastive learning—0
Information Maximization for Extreme Pose Face Recognition—0
Addressing Feature Suppression in Unsupervised Visual Representations—0
Information Theory-Guided Heuristic Progressive Multi-View Coding—0
Information Theory-Guided Heuristic Progressive Multi-View Coding—0
Inherit with Distillation and Evolve with Contrast: Exploring Class Incremental Semantic Segmentation Without Exemplar Memory—0
Injecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems—0
Injecting Text in Self-Supervised Speech Pretraining—0
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
InsCon:Instance Consistency Feature Representation via Self-Supervised Learning—0
InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal Multiview Sensory Input—0
Instance Adaptive Prototypical Contrastive Embedding for Generalized Zero Shot Learning—0
Instance Paradigm Contrastive Learning for Domain Generalization—0
Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning—0
Instance Segmentation with Cross-Modal Consistency—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