https://github.com/rt219/latentguard
공식 코드Latent Guard: a Safety Framework for Text-to-image Generation
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/rt219/the-emergence-of-objectness
공식 코드The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos
https://github.com/rthapa84/sleepfm-codebase
공식 코드https://github.com/ru-system-software-and-security/bppattack
공식 코드https://github.com/ruc-ai4math/premise-retrieval
공식 코드Assisting Mathematical Formalization with A Learning-based Premise Retriever
https://github.com/rucaibox/cogcl
공식 코드Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation
https://github.com/rucaibox/dclr
공식 코드Debiased Contrastive Learning of Unsupervised Sentence Representations
https://github.com/rucaibox/dpgnn
공식 코드https://github.com/rucaibox/eulerformer
공식 코드EulerFormer: Sequential User Behavior Modeling with Complex Vector Attention
https://github.com/rucaibox/ncl
공식 코드Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
https://github.com/rucaibox/wsdm2022-c2crs
공식 코드C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System
https://github.com/ruchikachavhan/amortized-invariance-learning-ssl
공식 코드Amortised Invariance Learning for Contrastive Self-Supervision
https://github.com/ruckbreasoning/openima
공식 코드https://github.com/ruiking04/coca
공식 코드https://github.com/ruiking04/roca
공식 코드RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data
https://github.com/ruiliu-ai/DivCo
공식 코드DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network
https://github.com/ruiqixu37/BTTS_ECAI2023
공식 코드Specializing Small Language Models towards Complex Style Transfer via Latent Attribute Pre-Training
https://github.com/ruixxxx/sfuda
공식 코드Unsupervised Cross-domain Pulmonary Nodule Detection without Source Data
https://github.com/ruizhipu-cs/group-dir
공식 코드Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression
https://github.com/runjia0124/coconet
공식 코드https://github.com/runjian-chen/co3
공식 코드CO^3: Cooperative Unsupervised 3D Representation Learning for Autonomous Driving
https://github.com/runnanchen/clip2scene
공식 코드CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP
https://github.com/rush2406/Self-Supervised-Learning-for-Fine-grained-Image-Classification
공식 코드Self-Supervised Learning for Fine-Grained Image Classification
https://github.com/rvimlab/ms_cs_contrseg
공식 코드Multi-scale and Cross-scale Contrastive Learning for Semantic Segmentation
https://github.com/rvl-lab-utoronto/video_similarity_search
공식 코드SLIC: Self-Supervised Learning with Iterative Clustering for Human Action Videos
https://github.com/rxlian/la-scl
공식 코드Learning Label Hierarchy with Supervised Contrastive Learning
https://github.com/ryanchankh/mcr2
공식 코드https://github.com/ryanchen227/lemon
공식 코드https://github.com/ryanhe312/LatentFace
공식 코드A Generative Framework for Self-Supervised Facial Representation Learning
https://github.com/ryanwangzf/medclip
공식 코드MedCLIP: Contrastive Learning from Unpaired Medical Images and Text
https://github.com/saandeepa93/FlowCon_OOD
공식 코드FlowCon: Out-of-Distribution Detection using Flow-Based Contrastive Learning
https://github.com/saic-fi/lfa
공식 코드https://github.com/sail-sg/mugs
공식 코드https://github.com/salah-zaiem/augmentations
공식 코드https://github.com/salesforce/CoMatch
공식 코드CoMatch: Semi-supervised Learning with Contrastive Graph Regularization
https://github.com/salesforce/CoST
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.