https://github.com/changyanchuan/trajcl
공식 코드Contrastive Trajectory Similarity Learning with Dual-Feature Attention
Papers with Code 아카이브
Contrastive Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 4
- 보존된 평가 기록
- 17
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/chao-ye/cm2
공식 코드https://github.com/chao1224/moleculestm
공식 코드Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing
https://github.com/chaoqi7/cmip-cil
공식 코드CMIP-CIL: A Cross-Modal Benchmark for Image-Point Class Incremental Learning
https://github.com/chaoyajiang/timix
공식 코드TiMix: Text-aware Image Mixing for Effective Vision-Language Pre-training
https://github.com/charles-ydd/trojanrag
공식 코드TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models
https://github.com/charlesge/openood-cadet
공식 코드CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning
https://github.com/charlesyou999648/MONA
공식 코드Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels
https://github.com/charlesyou999648/action
공식 코드ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast
https://github.com/charlesyou999648/arco
공식 코드Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
https://github.com/charlesyou999648/cfr
공식 코드Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations
https://github.com/charulp2499/fess_loss
공식 코드FESS Loss: Feature-Enhanced Spatial Segmentation Loss for Optimizing Medical Image Analysis
https://github.com/cheliu-computation/imitate-tmi2024
공식 코드IMITATE: Clinical Prior Guided Hierarchical Vision-Language Pre-training
https://github.com/chen-xdu/gmss
공식 코드GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition
https://github.com/chenfengxu714/pretram
공식 코드PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map
https://github.com/chengjin-git/hmil
공식 코드HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification
https://github.com/chengkai-liu/TwinCL
공식 코드TwinCL: A Twin Graph Contrastive Learning Model for Collaborative Filtering
https://github.com/chengtan9907/Hyperspherical-Consistency-Regularization
공식 코드https://github.com/chengw07/InfoTS
공식 코드Time Series Contrastive Learning with Information-Aware Augmentations
https://github.com/chengyui/nuwats
공식 코드NuwaTS: a Foundation Model Mending Every Incomplete Time Series
https://github.com/chenhao2345/GCL
공식 코드Joint Generative and Contrastive Learning for Unsupervised Person Re-identification
https://github.com/chenhao2345/ICE
공식 코드ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification
https://github.com/chenhao2345/gcl-extended
공식 코드Learning Invariance from Generated Variance for Unsupervised Person Re-identification
https://github.com/chenhaoxing/MACL_ICML2023
공식 코드Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
https://github.com/chenhongyiyang/ccop
공식 코드Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning
https://github.com/chenjie20/SSCDL
공식 코드https://github.com/chenjie20/cvcl
공식 코드Deep Multiview Clustering by Contrasting Cluster Assignments
https://github.com/chenkaisun/tetris
공식 코드Incorporating Task-specific Concept Knowledge into Script Learning
https://github.com/chenxi52/upa
공식 코드Uncertainty-Aware Pseudo-Label Filtering for Source-Free Unsupervised Domain Adaptation
https://github.com/chenxinan-fdu/colo
공식 코드COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarization
https://github.com/chesterdu/contrastive_summary
공식 코드Constructing Contrastive samples via Summarization for Text Classification with limited annotations
https://github.com/chincharles/u-emo
공식 코드UniEmoX: Cross-modal Semantic-Guided Large-Scale Pretraining for Universal Scene Emotion Perception
https://github.com/chingyaoc/DCL
공식 코드https://github.com/chingyaoc/rince
공식 코드https://github.com/chinmay5/vit_ae_plus_plus
공식 코드ViT-AE++: Improving Vision Transformer Autoencoder for Self-supervised Medical Image Representations
https://github.com/chipbautista/clrgaze
공식 코드CLRGaze: Contrastive Learning of Representations for Eye Movement Signals
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.