https://github.com/jiayou-chao/multi-domain-ocr
공식 코드Efficient Multi-domain Text Recognition Deep Neural Network Parameterization with Residual Adapters
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Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks.
<span style="color:grey; opacity: 0.6">( Image credit: Cross-stitch Networks for Multi-task Learning )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Efficient Multi-domain Text Recognition Deep Neural Network Parameterization with Residual Adapters
Adversarial Training for Code Retrieval with Question-Description Relevance Regularization
MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
R-LoRA: Random Initialization of Multi-Head LoRA for Multi-Task Learning
Acoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities
AudioLog: LLMs-Powered Long Audio Logging with Hybrid Token-Semantic Contrastive Learning
Unsupervised and Interpretable Domain Adaptation to Rapidly Filter Tweets for Emergency Services
Magnitude Invariant Parametrizations Improve Hypernetwork Learning
Compressed Hierarchical Representations for Multi-Task Learning and Task Clustering
The Effects of Multi-Task Learning on ReLU Neural Network Functions
Domain Aware Multi-Task Pretraining of 3D Swin Transformer for T1-weighted Brain MRI
Multi-task Learning Based Neural Bridging Reference Resolution
UIR-LoRA: Achieving Universal Image Restoration through Multiple Low-Rank Adaptation
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
Generalizing Natural Language Analysis through Span-relation Representations
Gated Task Interaction Framework for Multi-task Sequence Tagging
DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation
Cross-Model Transfer of Task Vectors via Few-Shot Orthogonal Alignment
Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles
Prompt Tuning with Soft Context Sharing for Vision-Language Models
Joint-Task Regularization for Partially Labeled Multi-Task Learning
On Privacy and Personalization in Cross-Silo Federated Learning
ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt
Allophant: Cross-lingual Phoneme Recognition with Articulatory Attributes
RetroMotion: Retrocausal Motion Forecasting Models are Instructable
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.