https://github.com/faltingsa/ssm
공식 코드Self-Supervised Pre-training with Symmetric Superimposition Modeling for Scene Text Recognition
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/fang6ang/disenpoi
공식 코드DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation
https://github.com/fangxiuwen/rahfl
공식 코드Robust Asymmetric Heterogeneous Federated Learning with Corrupted Clients
https://github.com/fanolabs/isointentbert-main
공식 코드https://github.com/faresbougourzi/ccfg-net
공식 코드Extremely Fine-Grained Visual Classification over Resembling Glyphs in the Wild
https://github.com/fatemehalipour/CGRclust
공식 코드CGRclust: Chaos Game Representation for Twin Contrastive Clustering of Unlabelled DNA Sequences
https://github.com/fchest/m3anet
공식 코드M3ANet: Multi-scale and Multi-Modal Alignment Network for Brain-Assisted Target Speaker Extraction
https://github.com/feiwangyuzhou/ternarycl
공식 코드Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning
https://github.com/feixiangzhou/smc-nca
공식 코드SMC-NCA: Semantic-guided Multi-level Contrast for Semi-supervised Temporal Action Segmentation
https://github.com/felixdjc/nlgad
공식 코드Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning
https://github.com/felixkreuk/UnsupSeg
공식 코드Self-Supervised Contrastive Learning for Unsupervised Phoneme Segmentation
https://github.com/fengbeiliu/semi-chest
공식 코드Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification
https://github.com/fengyuli2002/distribution-normalization
공식 코드Test-Time Distribution Normalization for Contrastively Learned Vision-language Models
https://github.com/ffhibnese/cpgc_vlp_universal_attacks
공식 코드https://github.com/fiy2w/cmda2022.superpolymerization
공식 코드https://github.com/fiy2w/mri_seq2seq
공식 코드Non-Adversarial Learning: Vector-Quantized Common Latent Space for Multi-Sequence MRI
https://github.com/flagai-open/flagai
공식 코드AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
https://github.com/flamiezhu/bcl
공식 코드Balanced Contrastive Learning for Long-Tailed Visual Recognition
https://github.com/floft/calda
공식 코드CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning
https://github.com/flyingsheepbin/pet-biometrics
공식 코드Dog nose print matching with dual global descriptor based on Contrastive Learning
https://github.com/fmlinks/domain
공식 코드Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning
https://github.com/fmthoker/skeleton-contrast
공식 코드https://github.com/fnzhan/monce
공식 코드https://github.com/foodfaust/CDR
공식 코드Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations
https://github.com/fpezzuti/multistage-finetuning
공식 코드Exploring the Effectiveness of Multi-stage Fine-tuning for Cross-encoder Re-rankers
https://github.com/fqhank/FuSTAL
공식 코드Full-Stage Pseudo Label Quality Enhancement for Weakly-supervised Temporal Action Localization
https://github.com/fragirla/mmssl-for-cvd-pred
공식 코드Enhancing Cardiovascular Disease Prediction through Multi-Modal Self-Supervised Learning
https://github.com/francescocappio/contrastive_open_world
공식 코드Contrastive Learning for Cross-Domain Open World Recognition
https://github.com/frank-xwang/CLD-UnsupervisedLearning
공식 코드Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination
https://github.com/franklinlingfeng/code-for-salcr
공식 코드Semantic-Aligned Learning with Collaborative Refinement for Unsupervised VI-ReID
https://github.com/frankzheng2022/taco
공식 코드TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning
https://github.com/friedrichor/UNITE
공식 코드Modality Curation: Building Universal Embeddings for Advanced Multimodal Information Retrieval
https://github.com/frost-yang-99/up-reid
공식 코드https://github.com/fudong03/mmst-vit
공식 코드https://github.com/fuen1590/phmdeeplearningprojects
공식 코드Supervised Contrastive Learning based Dual-Mixer Model for Remaining Useful Life Prediction
https://github.com/fukexue/pos-bert
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.