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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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/idegelis/torch-points3d-ssl-dcva
공식 코드Deep Unsupervised Learning for 3D ALS Point Cloud Change Detection
https://github.com/idstcv/coke
공식 코드Unsupervised Visual Representation Learning by Online Constrained K-Means
https://github.com/ielab/denseretriever-knowledge-acquisition
공식 코드Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition
https://github.com/ifding/MLKD
공식 코드Multi-level Knowledge Distillation via Knowledge Alignment and Correlation
https://github.com/iigroup/map
공식 코드MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model
https://github.com/illidanlab/MoCL-DK
공식 코드https://github.com/ilyassmoummad/dcase23_task5_scl
공식 코드Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning
https://github.com/ilyassmoummad/scl_icbhi2017
공식 코드Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning
https://github.com/im0qianqian/LAGCL
공식 코드Long-tail Augmented Graph Contrastive Learning for Recommendation
https://github.com/imageomics/treeoflife-toolbox
공식 코드BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning
https://github.com/imantdaunhawer/multimodal-contrastive-learning
공식 코드https://github.com/imyizhang/manifold-learn
공식 코드Supervised Stochastic Neighbor Embedding Using Contrastive Learning
https://github.com/indexfziq/clseg
공식 코드https://github.com/infinity7428/hANMCL
공식 코드Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning
https://github.com/intelligent-computing-lab-yale/nda_snn
공식 코드Neuromorphic Data Augmentation for Training Spiking Neural Networks
https://github.com/interdigitalinc/colde
공식 코드Supervised Contrastive Learning for Interpretable Long-Form Document Matching
https://github.com/intfloat/simkgc
공식 코드SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models
https://github.com/iqvianlp/llm-onto-infuse
공식 코드Towards Ontology-Enhanced Representation Learning for Large Language Models
https://github.com/iriscxy/target-aware-rwg
공식 코드Target-aware Abstractive Related Work Generation with Contrastive Learning
https://github.com/irislucent/motif-encoder
공식 코드https://github.com/ischenawei/dmnil
공식 코드Without Paired Labeled Data: An End-to-End Self-Supervised Paradigm for UAV-View Geo-Localization
https://github.com/isdling/predicttcl
공식 코드Joint Prediction of Meningioma Grade and Brain Invasion via Task-Aware Contrastive Learning
https://github.com/isha-67/cervicalcancerstudy
공식 코드Explainable Contrastive and Cost-Sensitive Learning for Cervical Cancer Classification
https://github.com/isjakewong/MIRACLE
공식 코드Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction
https://github.com/ispamm/GRAM
공식 코드https://github.com/ispc-lab/glc-plus
공식 코드https://github.com/ivanxie416/cmignet
공식 코드Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration
https://github.com/ivpb/qiepsm
공식 코드Quantum-inspired Embeddings Projection and Similarity Metrics for Representation Learning
https://github.com/iyyakuttiiyappan/CPLIP
공식 코드CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment
https://github.com/jackhck/subclass-balancing-contrastive-learning
공식 코드Subclass-balancing Contrastive Learning for Long-tailed Recognition
https://github.com/jackkoling/vtcc
공식 코드https://github.com/jacobzhaoziyuan/MMGL
공식 코드https://github.com/jaesoonbaik1213/dasc
공식 코드Distribution-Aware Robust Learning from Long-Tailed Data with Noisy Labels
https://github.com/jakaria08/explaining_shape_variability
공식 코드https://github.com/jam1ezhang/rankclip
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.