https://github.com/tahmedge/preqfas
공식 코드Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization
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/taimurhassan/continual_learning
공식 코드Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via Bayesian Deep Learning
https://github.com/takuo-h/GNN-for-OOKB
공식 코드Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach
https://github.com/talreiss/PANDA
공식 코드PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
https://github.com/talswisa/subtuning
공식 코드https://github.com/tanganke/pareto_set_learning
공식 코드Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion
https://github.com/tanganke/peta
공식 코드Parameter Efficient Multi-task Model Fusion with Partial Linearization
https://github.com/tanganke/subspace_fusion
공식 코드Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion
https://github.com/tanganke/weight-ensembling_moe
공식 코드Merging Multi-Task Models via Weight-Ensembling Mixture of Experts
https://github.com/tangxianfeng/PA-GNN
공식 코드Transferring Robustness for Graph Neural Network Against Poisoning Attacks
https://github.com/tank-jiang/cade4snn
공식 코드CADE: Cosine Annealing Differential Evolution for Spiking Neural Network
https://github.com/tanwimallick/TL-DCRNN
공식 코드Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting
https://github.com/tanyang1231/OTCE_Transferability_CVPR21
공식 코드OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations
https://github.com/taochenshh/hcp
공식 코드Hardware Conditioned Policies for Multi-Robot Transfer Learning
https://github.com/taokz/biomedgpt
공식 코드BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks
https://github.com/taoshen58/MaSP
공식 코드Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base
https://github.com/taoyang1122/MutualNet
공식 코드MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution
https://github.com/tarotis/building-an-endangered-language-resource-in-the-classroom
공식 코드Building an Endangered Language Resource in the Classroom: Universal Dependencies for Kakataibo
https://github.com/tartunlp/llammas
공식 코드Teaching Llama a New Language Through Cross-Lingual Knowledge Transfer
https://github.com/tarune28/modeling-t1-resting-state-mri-variants-using-convolutional-neural-networks-in-diagnosis-of-ocd
공식 코드Modeling T1 Resting-State MRI Variants Using Convolutional Neural Networks in Diagnosis of OCD
https://github.com/tatigabru/kaggle-rsna
공식 코드https://github.com/tbs17/MathBERT
공식 코드MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education
https://github.com/tdemin16/continual-layernorm-tuning
공식 코드On the Effectiveness of LayerNorm Tuning for Continual Learning in Vision Transformers
https://github.com/tdewolff/manatees
공식 코드Detection of manatee vocalisations using the Audio Spectrogram Transformer
https://github.com/techmonsterwang/illama
공식 코드https://github.com/technioncomputationalmrilab/colorectal_cancer_ai
공식 코드https://github.com/tekboart/CHB-image_classification_GradCam
공식 코드https://github.com/temphero/heronet
공식 코드HeroNet: A Hybrid Retrieval-Generation Network for Conversational Bots
https://github.com/tencent-ailab/hokoff
공식 코드Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks
https://github.com/tencentarc/pi-tuning
공식 코드$π$-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task Interpolation
https://github.com/tensorflow/lingvo/blob/master/lingvo/core/graddrop.py
공식 코드Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout
https://github.com/tensorflow/mesh
공식 코드ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
https://github.com/tensorflow/models
공식 코드Proper Reuse of Image Classification Features Improves Object Detection
https://github.com/tensorflow/models/tree/master/official/nlp/projects/mobilebert
공식 코드MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
https://github.com/tensorflow/privacy/tree/master/research/pate_2017
공식 코드Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
https://github.com/tensorflow/tensor2tensor
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.