https://github.com/aimagelab/pacscore
공식 코드Positive-Augmented Contrastive Learning for Image and Video Captioning Evaluation
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/aimat-lab/megan_global_explanations
공식 코드Global Concept Explanations for Graphs by Contrastive Learning
https://github.com/aindo-com/privacy-eval-paper
공식 코드Contrastive Learning-Based privacy metrics in Tabular Synthetic Datasets
https://github.com/airsplay/vimpac
공식 코드VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning
https://github.com/aixplain/NoRefER
공식 코드https://github.com/ajaychawda58/uadad
공식 코드Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings
https://github.com/akarimoon/xtask_mt
공식 코드Cross-Task Consistency Learning Framework for Multi-Task Learning
https://github.com/akaxlh/hccf
공식 코드https://github.com/albert-ma/costa
공식 코드Pre-train a Discriminative Text Encoder for Dense Retrieval via Contrastive Span Prediction
https://github.com/albertan017/hicl
공식 코드HICL: Hashtag-Driven In-Context Learning for Social Media Natural Language Understanding
https://github.com/aldraus/brainawarereplacementsforad
공식 코드Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer's Disease
https://github.com/alex-haochenli/racs
공식 코드https://github.com/alexander-schell/SigNICA
공식 코드Nonlinear Independent Component Analysis for Discrete-Time and Continuous-Time Signals
https://github.com/alexanderjaus/PPS
공식 코드https://github.com/alexlioralexli/noncontrastive-ssl
공식 코드Understanding Collapse in Non-Contrastive Siamese Representation Learning
https://github.com/alextamkin/viewmaker
공식 코드Viewmaker Networks: Learning Views for Unsupervised Representation Learning
https://github.com/aliabdari/NLP_to_rank_artistic_Metaverses
공식 코드https://github.com/alibaba-damo-academy/alice
공식 코드https://github.com/alibaba-damo-academy/fvlm
공식 코드Large-scale and Fine-grained Vision-language Pre-training for Enhanced CT Image Understanding
https://github.com/alibaba-damo-academy/self-supervised-anatomical-embedding-v2
공식 코드SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images
https://github.com/alibaba-mmai-research/TAdaConv
공식 코드ParamCrop: Parametric Cubic Cropping for Video Contrastive Learning
https://github.com/alibaba-mmai-research/molo
공식 코드MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition
https://github.com/alibaba/AliceMind
공식 코드From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression
https://github.com/alibaba/EasyNLP
공식 코드KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering
https://github.com/alibaba/EasyNLP/tree/master/examples/fewshot_learning
공식 코드Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning
https://github.com/alibaba/simcse-with-cards
공식 코드https://github.com/alibabaresearch/damo-convai
공식 코드https://github.com/alinlab/CSI
공식 코드CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
https://github.com/alinlab/object-aware-contrastive
공식 코드Object-aware Contrastive Learning for Debiased Scene Representation
https://github.com/alinlab/opencos
공식 코드OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data
https://github.com/alinlab/psco
공식 코드Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning
https://github.com/alinlab/s-clip
공식 코드S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions
https://github.com/alipay/Ant-Multi-Modal-Framework
공식 코드Dual-Modal Attention-Enhanced Text-Video Retrieval with Triplet Partial Margin Contrastive Learning
https://github.com/alireza-nasiri/SoundCLR
공식 코드SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification
https://github.com/allanchen95/code
공식 코드CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking
https://github.com/allanchen95/gccad
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.