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Contrastive Learning

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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.

<span style="color:grey; opacity: 0.6">(Image credit: Schroff et al. 2015)</span>

논문

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Contrastive Learning

논문

Source-Free Domain Adaptive Object Detection with Semantics Compensation

Breccia and basalt classification of thin sections of Apollo rocks with deep learning

SemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts

Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management

HapticCap: A Multimodal Dataset and Task for Understanding User Experience of Vibration Haptic Signals

SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation

Similarity-Guided Diffusion for Contrastive Sequential Recommendation

LLM-Driven Dual-Level Multi-Interest Modeling for Recommendation

Latent Space Consistency for Sparse-View CT Reconstruction

Self-supervised pretraining of vision transformers for animal behavioral analysis and neural encoding

RadiomicsRetrieval: A Customizable Framework for Medical Image Retrieval Using Radiomics Features

When Graph Contrastive Learning Backfires: Spectral Vulnerability and Defense in Recommendation

NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation

Pun Intended: Multi-Agent Translation of Wordplay with Contrastive Learning and Phonetic-Semantic Embeddings

DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation

From ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering Recommendation

Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations

CultureCLIP: Empowering CLIP with Cultural Awareness through Synthetic Images and Contextualized Captions

Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation

Neural-Driven Image Editing

Helping CLIP See Both the Forest and the Trees: A Decomposition and Description Approach

Weakly-supervised Contrastive Learning with Quantity Prompts for Moving Infrared Small Target Detection

DARTS: A Dual-View Attack Framework for Targeted Manipulation in Federated Sequential Recommendation

Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System

FOCUS: Fine-grained Optimization with Semantic Guided Understanding for Pedestrian Attributes Recognition

Enhancing Homophily-Heterophily Separation: Relation-Aware Learning in Heterogeneous Graphs

Detection of Breast Cancer Lumpectomy Margin with SAM-incorporated Forward-Forward Contrastive Learning

FedSC: Federated Learning with Semantic-Aware Collaboration

V2X-REALM: Vision-Language Model-Based Robust End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling

Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features

PeakNetFP: Peak-based Neural Audio Fingerprinting Robust to Extreme Time Stretching

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation

AgentStealth: Reinforcing Large Language Model for Anonymizing User-generated Text

Directed Link Prediction using GNN with Local and Global Feature Fusion

scMamba: A Scalable Foundation Model for Single-Cell Multi-Omics Integration Beyond Highly Variable Feature Selection