https://github.com/qtacierp/prior
공식 코드PRIOR: Prototype Representation Joint Learning from Medical Images and Reports
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/quanshr/augcon
공식 코드Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse Granularity
https://github.com/quantacode/contrastive-finetuning
공식 코드On the Importance of Distractors for Few-Shot Classification
https://github.com/quanvu0996/efficientrec
공식 코드https://github.com/quanweiliu/knowcl
공식 코드A Universal Knowledge Embedded Contrastive Learning Framework for Hyperspectral Image Classification
https://github.com/quhongyu/cluspro
공식 코드Learning Clustering-based Prototypes for Compositional Zero-shot Learning
https://github.com/qybc/alifuse
공식 코드AliFuse: Aligning and Fusing Multi-modal Medical Data for Computer-Aided Diagnosis
https://github.com/qyp9909/indirec
공식 코드Intent-aware Diffusion with Contrastive Learning for Sequential Recommendation
https://github.com/qz-wang/act
공식 코드Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment
https://github.com/r-cui/ViGA
공식 코드Video Moment Retrieval from Text Queries via Single Frame Annotation
https://github.com/raghavlite/GenEOL
공식 코드GenEOL: Harnessing the Generative Power of LLMs for Training-Free Sentence Embeddings
https://github.com/raghavlite/b3
공식 코드Breaking the Batch Barrier (B3) of Contrastive Learning via Smart Batch Mining
https://github.com/rain-neuromorphics/energy-based-learning
공식 코드Energy-based learning algorithms for analog computing: a comparative study
https://github.com/rajatkoner08/oodformer
공식 코드https://github.com/rajesh-lab/symile
공식 코드Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities
https://github.com/ramdrop/edgevl
공식 코드Self-Adapting Large Visual-Language Models to Edge Devices across Visual Modalities
https://github.com/rana2149/actnetformer
공식 코드ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in Videos
https://github.com/ratschlab/genomic-gnn
공식 코드Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs
https://github.com/ratschlab/ncl
공식 코드Neighborhood Contrastive Learning Applied to Online Patient Monitoring
https://github.com/rattlesnakey/ShifCon
공식 코드ShifCon: Enhancing Non-Dominant Language Capabilities with a Shift-based Contrastive Framework
https://github.com/rattlesnakey/definition-gneration-contrastive
공식 코드https://github.com/rattlesnakey/q-mckt
공식 코드https://github.com/ravikiranrao/mtclar-fsl
공식 코드Efficient Labelling of Affective Video Datasets via Few-Shot & Multi-Task Contrastive Learning
https://github.com/raymin0223/patch-mix_contrastive_learning
공식 코드https://github.com/raymin0223/self-contrastive-learning
공식 코드Self-Contrastive Learning: Single-viewed Supervised Contrastive Framework using Sub-network
https://github.com/razzu/cmc-cmkm
공식 코드Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity Recognition
https://github.com/rdangovs/essl
공식 코드https://github.com/rdz98/unsup-vvs
공식 코드https://github.com/reasat/data_efficient_cl
공식 코드Data Efficient Contrastive Learning in Histopathology using Active Sampling
https://github.com/regan-zhang/digpro
공식 코드Towards Efficient and Effective Deep Clustering with Dynamic Grouping and Prototype Aggregation
https://github.com/regan-zhang/hadis
공식 코드Deep Clustering with Diffused Sampling and Hardness-aware Self-distillation
https://github.com/regan-zhang/pici
공식 코드https://github.com/reinforest-team/reinforest
공식 코드REINFOREST: Reinforcing Semantic Code Similarity for Cross-Lingual Code Search Models
https://github.com/reneeye/const
공식 코드https://github.com/renj-xu/negsc
공식 코드https://github.com/renwang435/cyborgs
공식 코드CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.