https://github.com/gt4sd/multitask_text_and_chemistry_t5
공식 코드Unifying Molecular and Textual Representations via Multi-task Language Modelling
Papers with Code 아카이브
Transfer Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 6
- 보존된 평가 기록
- 10
Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.
<span style="color:grey; opacity: 0.6">( Image credit: Subodh Malgonde )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/guanghaozhu663/astmatch
공식 코드https://github.com/guanghuifu/medical_dino_eval
공식 코드https://github.com/guanjiyang/sac
공식 코드Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks
https://github.com/guanjiyang/sac_jc
공식 코드https://github.com/guanqun-yang/fewvulnerability
공식 코드https://github.com/guijinSON/KorFin_ASC
공식 코드https://github.com/guitld/Transfer-Learning-with-Joint-Fine-Tuning-for-Multimodal-Sentiment-Analysis
공식 코드Transfer Learning with Joint Fine-Tuning for Multimodal Sentiment Analysis
https://github.com/guoxuxu/LOANT
공식 코드Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection
https://github.com/gustafvh/SignInterpreterSSL
공식 코드Interpretation of Swedish Sign Language using Convolutional Neural Networks and Transfer Learning
https://github.com/gustavozf/EGC_Z_dataset
공식 코드Automatic Chronic Degenerative Diseases Identification Using Enteric Nervous System Images
https://github.com/guxm2021/alt_speechbrain
공식 코드Transfer Learning of wav2vec 2.0 for Automatic Lyric Transcription
https://github.com/gvalvano/multiscale-adversarial-attention-gates
공식 코드Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates
https://github.com/gyguo/pixeldistillation
공식 코드Pixel Distillation: A New Knowledge Distillation Scheme for Low-Resolution Image Recognition
https://github.com/habibslim/dkt-for-cil
공식 코드Dataset Knowledge Transfer for Class-Incremental Learning without Memory
https://github.com/habla-liaa/ser-with-w2v2
공식 코드Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings
https://github.com/hadijomaa/dataset2vec
공식 코드https://github.com/hahahawu/CSAGN
공식 코드CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling
https://github.com/haidnguyen0909/mwgrad
공식 코드Multiple Wasserstein Gradient Descent Algorithm for Multi-Objective Distributional Optimization
https://github.com/haiduo/deepkd
공식 코드DeepKD: A Deeply Decoupled and Denoised Knowledge Distillation Trainer
https://github.com/hainn2803/s-otdd
공식 코드Lightspeed Geometric Dataset Distance via Sliced Optimal Transport
https://github.com/hal-lucination/segfuse
공식 코드360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse Network
https://github.com/hamanhbui/reliable_ssl_baselines
공식 코드Benchmark for Uncertainty & Robustness in Self-Supervised Learning
https://github.com/hamish-haggerty/base_rbt
공식 코드Self-supervised learning for skin cancer diagnosis with limited training data
https://github.com/hananshafi/medcontext
공식 코드MedContext: Learning Contextual Cues for Efficient Volumetric Medical Segmentation
https://github.com/hankook/SLA
공식 코드Self-supervised Label Augmentation via Input Transformations
https://github.com/hankook/augself
공식 코드Improving Transferability of Representations via Augmentation-Aware Self-Supervision
https://github.com/hannandarryl/ManyModalQA
공식 코드ManyModalQA: Modality Disambiguation and QA over Diverse Inputs
https://github.com/hansd410/cross-lingfnd
공식 코드Cross-lingual Transfer Learning for Fake News Detector in a Low-Resource Language
https://github.com/haomengz/hyperpc
공식 코드https://github.com/haoranzhuexplorer/ad-l-jepa-release
공식 코드https://github.com/haoweiz23/ship
공식 코드Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning
https://github.com/haoyangliASTAPLE/DDCS_MI
공식 코드A Sample-Level Evaluation and Generative Framework for Model Inversion Attacks
https://github.com/harboryuan/ovsam
공식 코드Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively
https://github.com/harmonbhasin/curriculum_learning_icl
공식 코드https://github.com/harshs27/neural-graphical-models
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.