https://github.com/neurospin-projects/2024_rlouiset_sep_clr
공식 코드Separating common from salient patterns with Contrastive Representation Learning
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/neutralpronoun/general-gcl
공식 코드Topology Only Pre-Training: Towards Generalised Multi-Domain Graph Models
https://github.com/ngbountos/deepcubevolcano
공식 코드Self-supervised Contrastive Learning for Volcanic Unrest Detection
https://github.com/ngocminhta/faid
공식 코드https://github.com/nguoikhongnao/real_oecl
공식 코드Understanding normalization in contrastive representation learning and out-of-distribution detection
https://github.com/nguyentthong/CLNTM
공식 코드https://github.com/nguyentthong/adaptive_contrastive_mrhp
공식 코드Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions
https://github.com/nhhoang96/multicl_slot_induction
공식 코드Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive Learning
https://github.com/nickeopti/bach-contrastive-segmentation
공식 코드https://github.com/nicolas1203/ossgcl
공식 코드Contrastive Learning for Online Semi-Supervised General Continual Learning
https://github.com/nidryen-zh/questionerawareresponder
공식 코드Personalized LLM for Generating Customized Responses to the Same Query from Different Users
https://github.com/niiceMing/CMTA
공식 코드Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning
https://github.com/ninatu/learning_by_sorting
공식 코드Learning by Sorting: Self-supervised Learning with Group Ordering Constraints
https://github.com/ninecl/dekg-ilp
공식 코드Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction
https://github.com/nisarahmedrana/arabicmlec
공식 코드https://github.com/nishadsinghi/cleanclip
공식 코드CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning
https://github.com/nishantrai18/CoCon
공식 코드https://github.com/nitkannen/contraste
공식 코드https://github.com/niuchuangnn/SPICE
공식 코드https://github.com/niuchuangnn/imsvd
공식 코드Information-Maximized Soft Variable Discretization for Self-Supervised Image Representation Learning
https://github.com/nju-websoft/cca
공식 코드Knowledge Graph Error Detection with Contrastive Confidence Adaption
https://github.com/nju-websoft/sckd
공식 코드Serial Contrastive Knowledge Distillation for Continual Few-shot Relation Extraction
https://github.com/njucckevin/ads-cap
공식 코드ADS-Cap: A Framework for Accurate and Diverse Stylized Captioning with Unpaired Stylistic Corpora
https://github.com/nku-hlt/pb-dsr
공식 코드Enhancing Dysarthric Speech Recognition for Unseen Speakers via Prototype-Based Adaptation
https://github.com/nlpods/layerattpooler
공식 코드https://github.com/nlpwm-whu/tadner
공식 코드Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition
https://github.com/noahcao/disentanglement_lib_med
공식 코드An Empirical Study on Disentanglement of Negative-free Contrastive Learning
https://github.com/noanonkes/Hallucination-Detection-in-LLMs
공식 코드Leveraging Graph Structures to Detect Hallucinations in Large Language Models
https://github.com/nokia-bell-labs/papagei-foundation-model
공식 코드PaPaGei: Open Foundation Models for Optical Physiological Signals
https://github.com/nomewang/m3dm
공식 코드https://github.com/nomewang/mcf
공식 코드https://github.com/noorahmedds/orco
공식 코드https://github.com/npucvr/wscuod
공식 코드Weakly-supervised Contrastive Learning for Unsupervised Object Discovery
https://github.com/nswa17/mmcl
공식 코드Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data
https://github.com/nttcslab/msm-mae
공식 코드https://github.com/ntuddsnlab/srgcl
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.