Transfer Learning
6개 벤치마크 · 논문 11,200편 · 이 태스크의 논문 보기 →
Benchmarks
Office-Home
COCO70
Most implemented
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Universal Language Model Fine-tuning for Text Classification
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
High Quality Monocular Depth Estimation via Transfer Learning
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Papers
Temporal-Difference Learning for Dragonchess
Our research investigates how two adaptive AI methods, evolutionary transfer learning and TD(lambda), perform in the three-dimensional chess environment Dragonchess. The game challenges players with its unique board stru…
Transfer LearningLangevin-Informed Transfer Learning: Replacing Target Samples by Black-Box Feedback
Many scientific and machine learning systems, from molecular dynamics to diffusion models and beyond, are governed by stochastic dynamics with low-dimensional structure, evolving on slow timescales. However, target traje…
Representation LearningTransfer LearningFleet-To-Lab: A Transfer Learning Framework For Lunar Rover Slippage Estimation Via Model Fusion
Accurate wheel slip estimation is essential for autonomous lunar rover mobility and navigation. Machine Learning models trained on terrestrial data generalize poorly to lunar terrain, and real lunar datasets are scarce d…
Transfer LearningVery Exciting: Zero-Shot Model Predictive Control of Buildings via Excitation-Based Generalized Transfer Learning Models
The widespread adoption of data-driven, energy-efficient model predictive control (MPC) in buildings remains hindered by substantial effort to collect data and train models for individual buildings. Transfer learning (TL…
Transfer LearningMulti-Pass, Multi-View Blended Learning for High-Fidelity Volumetric CT Synthesis from Chest X-Rays
Reconstructing volumetric Computed Tomography (CT) from a single 2D chest radiograph (CXR) is an ill-posed inverse problem, further complicated by the scarcity of paired CXR-CT training data. Prior approaches address thi…
Domain AdaptationTransfer LearningGenerative multi-domain transfer learning for fault detection in data-scarce wind turbines
Normal behavior models have shown promise for reliable fault detection in wind turbines. However, these unsupervised anomaly detection models require sufficient fault-free training data to learn the normal operation beha…
Unsupervised Anomaly DetectionTransfer Learning