https://github.com/fpv-iplab/rulstm
Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video
Papers with Code 아카이브
Unsupervised Pre-training
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/ghazalkhalighinejad/approximating-cky
Multilingual Constituency Parsing with Self-Attention and Pre-Training
https://github.com/google-research/bigbird
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
https://github.com/harshraj22/DeepLearning/tree/main/implementations/TabTransformer
TabTransformer: Tabular Data Modeling Using Contextual Embeddings
https://github.com/hewanshrestha/why-self-supervision-in-time
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data
https://github.com/huggingface/transformers
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
https://github.com/iinemo/lm-polygraph
https://github.com/josselinsomervilleroberts/ptsl
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning
https://github.com/jpedrojpedro/transformers-tabular-data
TabTransformer: Tabular Data Modeling Using Contextual Embeddings
https://github.com/jrzaurin/pytorch-widedeep
TabTransformer: Tabular Data Modeling Using Contextual Embeddings
https://github.com/khangt1k25/Contrastive-Segmentation
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals
https://github.com/kiyoungkim1/LMkor
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
https://github.com/lifuguan/UPDETR-mmdet
UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
https://github.com/lizzylizzylizzy/evolvingattention
A Transformer-based Framework for Multivariate Time Series Representation Learning
https://github.com/m3hrdadfi/news-headline-generation
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
https://github.com/m3hrdadfi/wiki-summary
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
https://github.com/mailong25/vietnamese-speech-recognition
https://github.com/manujosephv/pytorch_tabular
TabTransformer: Tabular Data Modeling Using Contextual Embeddings
https://github.com/monsta-hd/boltzmann-machines
How far can we go without convolution: Improving fully-connected networks
https://github.com/pkuyym/evolvingattention
A Transformer-based Framework for Multivariate Time Series Representation Learning
https://github.com/pokaxpoka/b_pref
https://github.com/pwc-1/Paper-9/tree/main/1/wav2vec2
https://github.com/pytorch/fairseq/blob/master/fairseq/models/wav2vec.py
https://github.com/radi-cho/gatedtabtransformer
TabTransformer: Tabular Data Modeling Using Contextual Embeddings
https://github.com/rail-berkeley/supe
https://github.com/rickyHong/Boltzman-repl
How far can we go without convolution: Improving fully-connected networks
https://github.com/seleucia/goca
A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning
https://github.com/servicenow/seasonal-contrast
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data
https://github.com/shangeth/wavencoder
https://github.com/stanislavfort/exploring_the_limits_of_OOD_detection
https://github.com/sustainlab-group/SatMAE
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery
https://github.com/sxjscience/GluonNLP-Slot-Filling
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents
https://github.com/thomaslu2000/incremental-parsing-representations
Multilingual Constituency Parsing with Self-Attention and Pre-Training
https://github.com/timeseriesAI/tsai/blob/main/tsai/models/TST.py
A Transformer-based Framework for Multivariate Time Series Representation Learning
https://github.com/timeseriesAI/tsai/blob/main/tsai/models/TabTransformer.py
TabTransformer: Tabular Data Modeling Using Contextual Embeddings
https://github.com/tomas-gajarsky/facetorch
Pre-training strategies and datasets for facial representation learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.