https://github.com/PKUnlp-icler/ContrastivePruning
공식 코드From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression
Papers with Code 아카이브
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/POSTECH-CVLab/PyTorch-StudioGAN
공식 코드ContraGAN: Contrastive Learning for Conditional Image Generation
https://github.com/PaddlePaddle/ERNIE
공식 코드ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/text_generation/unimo-text
공식 코드UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning
https://github.com/PangYunsheng8/CGIPool
공식 코드https://github.com/Paranioar/DBL
공식 코드Deep Boosting Learning: A Brand-new Cooperative Approach for Image-Text Matching
https://github.com/PardoAlejo/LearningToCut
공식 코드https://github.com/ParitoshParmar/Fitness-AQA
공식 코드Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment
https://github.com/Pliploop/SemiSupCon
공식 코드Semi-Supervised Contrastive Learning of Musical Representations
https://github.com/Pointcept/Pointcept
공식 코드Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learning
https://github.com/PolymathicAI/AstroCLIP
공식 코드https://github.com/PsyberSecLab/Fine-Tuning-and-Explainability-for-Phishing-Detection
공식 코드https://github.com/QingtaoPan/DuSSS
공식 코드https://github.com/QiufanLin/CLAP-I
공식 코드CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimation
https://github.com/RaghavSinghal10/M3CoL
공식 코드Harnessing Shared Relations via Multimodal Mixup Contrastive Learning for Multimodal Classification
https://github.com/Regan-Zhang/DXMC
공식 코드https://github.com/RikoLi/DCAC
공식 코드Unleashing the Potential of Pre-Trained Diffusion Models for Generalizable Person Re-Identification
https://github.com/RikoLi/PCL-CLIP
공식 코드Prototypical Contrastive Learning-based CLIP Fine-tuning for Object Re-identification
https://github.com/RuihongQiu/DuoRec
공식 코드Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation
https://github.com/RuihongQiu/MMInfoRec
공식 코드Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation
https://github.com/RuoyuChen10/CCL-FSOD
공식 코드https://github.com/SCUT-AILab/CPGA
공식 코드Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation
https://github.com/SHERSONH/DWCL
공식 코드DWCL: Dual-Weighted Contrastive Learning for Multi-View Clustering
https://github.com/SPEECHCOG/PFML
공식 코드PFML: Self-Supervised Learning of Time-Series Data Without Representation Collapse
https://github.com/SStarCCat/HM4SR
공식 코드Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation
https://github.com/ShambhaviCodes/CLIPOT
공식 코드Words Matter: Leveraging Individual Text Embeddings for Code Generation in CLIP Test-Time Adaptation
https://github.com/Shathe/SemiSeg-Contrastive
공식 코드https://github.com/Shef-AIRE/MeDSLIP
공식 코드MeDSLIP: Medical Dual-Stream Language-Image Pre-training for Fine-grained Alignment
https://github.com/Shen-Lab/GraphCL
공식 코드https://github.com/Shen-Lab/GraphCL_Automated
공식 코드https://github.com/ShenZheng2000/SAPNet-for-image-deraining
공식 코드SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Deraining
https://github.com/Siki-cloud/PairCFR
공식 코드https://github.com/Simon-Lepage/LRVSF-Benchmark
공식 코드LRVS-Fashion: Extending Visual Search with Referring Instructions
https://github.com/Sjyhne/ContrastiveGeoSeg
공식 코드A Contrastive Learning Scheme with Transformer Innate Patches
https://github.com/Skyy93/Sample4Geo
공식 코드Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation
https://github.com/SlicerDMRI/SupWMA
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.