https://github.com/chiyuzhang94/contrastive_learning_time-series_e2e
공식 코드What Constitutes Good Contrastive Learning in Time-Series Forecasting?
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/chl781/Augmented_spectral_contrastive_learning
공식 코드Consistency of augmentation graph and network approximability in contrastive learning
https://github.com/chohoonhee/ev-wsss
공식 코드Finding Meaning in Points: Weakly Supervised Semantic Segmentation for Event Cameras
https://github.com/chongli17/crosslingualalignment
공식 코드https://github.com/chongyi-zheng/stable_contrastive_rl
공식 코드Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data
https://github.com/chris210634/metric-learning-using-contextual-similarity
공식 코드Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization
https://github.com/chrisisking/rule-by-example
공식 코드Rule By Example: Harnessing Logical Rules for Explainable Hate Speech Detection
https://github.com/christos42/CLDR_CLNER_models
공식 코드Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning
https://github.com/chromaprim/SHGCL_DTI_GWT
공식 코드https://github.com/chuangua/contrastivelossmlml
공식 코드Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels
https://github.com/chujiezheng/click
공식 코드Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning
https://github.com/chumingqian/CycleGuardian
공식 코드https://github.com/chymaera96/neuralsampleid
공식 코드Refining music sample identification with a self-supervised graph neural network
https://github.com/chz816/esacl
공식 코드Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization
https://github.com/cimy-wang/fusdreamer
공식 코드FusDreamer: Label-efficient Remote Sensing World Model for Multimodal Data Classification
https://github.com/circle-ming/multicsr
공식 코드https://github.com/cisnlp/translico
공식 코드https://github.com/cjdbehumble/miccai2023-vvc-screening
공식 코드Progressive Attention Guidance for Whole Slide Vulvovaginal Candidiasis Screening
https://github.com/cl4ctr/cl4ctr
공식 코드https://github.com/clad23/clad
공식 코드CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive Learning
https://github.com/clarence-cv/contexuality-gcd
공식 코드Contextuality Helps Representation Learning for Generalized Category Discovery
https://github.com/claudiashu/una
공식 코드Unsupervised hard Negative Augmentation for contrastive learning
https://github.com/cliveyn/scstory
공식 코드SCStory: Self-supervised and Continual Online Story Discovery
https://github.com/clmfap/clmfap
공식 코드https://github.com/clott3/sib-cl
공식 코드Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science
https://github.com/cloudcatcher888/rcl
공식 코드https://github.com/cloudcjf/ccl
공식 코드CCL: Continual Contrastive Learning for LiDAR Place Recognition
https://github.com/cloudy1225/blnn
공식 코드Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised Learning
https://github.com/cm8908/contrastcad
공식 코드ContrastCAD: Contrastive Learning-based Representation Learning for Computer-Aided Design Models
https://github.com/cmhimself/quest
공식 코드Querying functional and structural niches on spatial transcriptomics data
https://github.com/cocoxili/cmpc
공식 코드Unsupervised Voice-Face Representation Learning by Cross-Modal Prototype Contrast
https://github.com/codegallery24/cpcl
공식 코드https://github.com/coder-yu/selfrec
공식 코드XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation
https://github.com/coderwzw/arlib
공식 코드Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks
https://github.com/codesharea/eyelearn
공식 코드Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images
https://github.com/colaudiolab/fame24_solution
공식 코드Contrastive Learning-based Chaining-Cluster for Multilingual Voice-Face Association
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.