https://github.com/nikitakit/self-attentive-parser
공식 코드Multilingual Constituency Parsing with Self-Attention and Pre-Training
Papers with Code 아카이브
Unsupervised Pre-training
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Pre-training a neural network using unsupervised (self-supervised) auxiliary tasks on unlabeled data.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/owen-yang-18/brainnn-pretrain
공식 코드PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis
https://github.com/philkr/magic_init
공식 코드Data-dependent Initializations of Convolutional Neural Networks
https://github.com/pixta-dev/labteam
공식 코드MAGNeto: An Efficient Deep Learning Method for the Extractive Tags Summarization Problem
https://github.com/pytorch/fairseq
공식 코드https://github.com/pytorch/fairseq/blob/f3b6f5817fbee59057ae2506f01502ea3c301b4b/examples/wav2vec/README.md
공식 코드Self-training and Pre-training are Complementary for Speech Recognition
https://github.com/qwenlm/qwen3-embedding
공식 코드Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
https://github.com/rinuboney/fpac
공식 코드https://github.com/rinuboney/keyq
공식 코드https://github.com/ritaranx/bmretriever
공식 코드BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers
https://github.com/rll-research/bpref
공식 코드https://github.com/rll-research/url_benchmark
공식 코드https://github.com/rossumai/docile
공식 코드DocILE Benchmark for Document Information Localization and Extraction
https://github.com/ryansteed/ieat
공식 코드Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases
https://github.com/salmanmaq/segmentationNetworks
공식 코드m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural Networks
https://github.com/seohongpark/hilp
공식 코드https://github.com/seohongpark/hiql
공식 코드HIQL: Offline Goal-Conditioned RL with Latent States as Actions
https://github.com/seohongpark/metra
공식 코드METRA: Scalable Unsupervised RL with Metric-Aware Abstraction
https://github.com/shamdan17/likra
공식 코드https://github.com/sid7954/functional-regularization
공식 코드Functional Regularization for Representation Learning: A Unified Theoretical Perspective
https://github.com/tensorflow/models/tree/master/official/projects/video_ssl
공식 코드https://github.com/thu-ml/CEURL
공식 코드PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning
https://github.com/thuml/ContextWM
공식 코드Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning
https://github.com/tomMoral/post_training
공식 코드https://github.com/valeoai/obow
공식 코드OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning
https://github.com/wangwenhao0716/isc-track1-submission
공식 코드D$^2$LV: A Data-Driven and Local-Verification Approach for Image Copy Detection
https://github.com/wangwenhao0716/isc-track2-submission
공식 코드Bag of Tricks and A Strong baseline for Image Copy Detection
https://github.com/weathon/vcos
공식 코드ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation
https://github.com/wolo-wolo/FSFM
공식 코드https://github.com/wvangansbeke/Unsupervised-Semantic-Segmentation
공식 코드Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals
https://github.com/yinjunbo/proposalcontrast
공식 코드ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection
https://github.com/younggyoseo/apv
공식 코드Reinforcement Learning with Action-Free Pre-Training from Videos
https://github.com/zhang-can/up-tal
공식 코드Unsupervised Pre-training for Temporal Action Localization Tasks
https://github.com/zhengxiangshi/powerfulpromptft
공식 코드Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner
https://github.com/zhudanhao/g-gnn
공식 코드Pre-train and Learn: Preserve Global Information for Graph Neural Networks
https://github.com/zhujiwei111/space
공식 코드SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.