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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/1adrianb/unsupervised-face-representation

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Pre-training strategies and datasets for facial representation learning

https://github.com/935963004/labram

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Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI

https://github.com/AsaCooperStickland/Bert-n-Pals

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BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning

https://github.com/Barabasi-Lab/AI-Bind

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AI-Bind: Improving Binding Predictions for Novel Protein Targets and Ligands

https://github.com/CVMI-Lab/SlotCon

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Self-Supervised Visual Representation Learning with Semantic Grouping

https://github.com/DFKI-NLP/TRE

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Improving Relation Extraction by Pre-trained Language Representations

https://github.com/DFKI-NLP/tacrev

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TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task

https://github.com/DSPsleeporg/smiles-transformer

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SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery

https://github.com/DengpanFu/LUPerson

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Unsupervised Pre-training for Person Re-identification

https://github.com/Dongqi-Fan/ConStyle_v2

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ConStyle v2: A Strong Prompter for All-in-One Image Restoration

https://github.com/ElementAI/seasonal-contrast

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Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data

https://github.com/ItzikMalkiel/MML

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MML: Maximal Multiverse Learning for Robust Fine-Tuning of Language Models

https://github.com/Johnathan-Xie/adaptive-temperature-scaling

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Calibrating Language Models with Adaptive Temperature Scaling

https://github.com/KangchengLiu/FAC_Foreground_Aware_Contrast

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Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast

https://github.com/MSU-MLSys-Lab/CATE

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CATE: Computation-aware Neural Architecture Encoding with Transformers

https://github.com/NVIDIA/Megatron-LM

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End-to-End Training of Neural Retrievers for Open-Domain Question Answering

https://github.com/YihengZhang-CV/SeCo-Sequence-Contrastive-Learning

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SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning

https://github.com/alon-albalak/data-selection-survey

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A Survey on Data Selection for Language Models

https://github.com/amzn/pretraining-or-self-training

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Rethinking Semi-supervised Learning with Language Models

https://github.com/asappresearch/sew

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Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition

https://github.com/athena-team/athena

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A Further Study of Unsupervised Pre-training for Transformer Based Speech Recognition

https://github.com/broadinstitute/ml4h

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Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling

https://github.com/caglar/structured_mlp

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Knowledge Matters: Importance of Prior Information for Optimization

https://github.com/celehs/latte

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LATTE: Label-efficient Incident Phenotyping from Longitudinal Electronic Health Records

https://github.com/chantalmp/unsupervised-pre-training-on-patient-population-graphs-for-patient-level-predictions

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Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions

https://github.com/chantalmp/unsupervised_pre-training_of_graph_transformers_on_patient_population_graphs

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Unsupervised pre-training of graph transformers on patient population graphs

https://github.com/chatterjeeayan/ai-bind

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AI-Bind: Improving Binding Predictions for Novel Protein Targets and Ligands

https://github.com/cnaigithub/SpeechDewarping

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Unsupervised Pre-Training For Data-Efficient Text-to-Speech On Low Resource Languages

https://github.com/cvg/depthsplat

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DepthSplat: Connecting Gaussian Splatting and Depth

https://github.com/cvi-szu/linly

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Dynamic data sampler for cross-language transfer learning in large language models

https://github.com/dddzg/up-detr

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UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

https://github.com/deepmodeling/crystalformer

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CrystalFormer-RL: Reinforcement Fine-Tuning for Materials Design

https://github.com/deepopinion/greedy-layer-pruning

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Greedy-layer Pruning: Speeding up Transformer Models for Natural Language Processing

https://github.com/dengpanfu/luperson-nl

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Large-Scale Pre-training for Person Re-identification with Noisy Labels

https://github.com/devalab/swinfuse

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Self-Supervised Modality-Agnostic Pre-Training of Swin Transformers

https://github.com/facebookresearch/CodeGen/blob/main/docs/dobf.md

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DOBF: A Deobfuscation Pre-Training Objective for Programming Languages

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