Transfer Learning
2857 papers with code • 7 benchmarks • 15 datasets
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.
( Image credit: Subodh Malgonde )
Benchmarks
These leaderboards are used to track progress in Transfer Learning
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Libraries
Use these libraries to find Transfer Learning models and implementationsDatasets
KITTI
Office-Home
Sims4Action
MFRC
CARLANE Benchmark
BanglaLekha-Isolated
SpaRTUN
fluocells
Retinal Fundus MultiDisease Image Dataset (RFMiD)
Subtasks
Latest papers with no code
Metric Learning for 3D Point Clouds Using Optimal Transport
Learning embeddings of any data largely depends on the ability of the target space to capture semantic rela- tions.
Hypergraph-enhanced Dual Semi-supervised Graph Classification
In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs.
Large Language Models for Cyber Security: A Systematic Literature Review
Overall, our survey provides a comprehensive overview of the current state-of-the-art in LLM4Security and identifies several promising directions for future research.
Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation
Diffusion models are a powerful generative framework, but come with expensive inference.
Exploring Vision Transformers for 3D Human Motion-Language Models with Motion Patches
To build a cross-modal latent space between 3D human motion and language, acquiring large-scale and high-quality human motion data is crucial.
Deep learning-based variational autoencoder for classification of quantum and classical states of light
Advancements in optical quantum technologies have been enabled by the generation, manipulation, and characterization of light, with identification based on its photon statistics.
Encoder-Decoder Framework for Interactive Free Verses with Generation with Controllable High-Quality Rhyming
We propose a novel fine-tuning approach that prepends the rhyming word at the start of each lyric, which allows the critical rhyming decision to be made before the model commits to the content of the lyric (as during reverse language modeling), but maintains compatibility with the word order of regular PLMs as the lyric itself is still generated in left-to-right order.
Enriched BERT Embeddings for Scholarly Publication Classification
With the rapid expansion of academic literature and the proliferation of preprints, researchers face growing challenges in manually organizing and labeling large volumes of articles.
ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation
Cross-modal knowledge transfer enhances point cloud representation learning in LiDAR semantic segmentation.
Predicting Lung Disease Severity via Image-Based AQI Analysis using Deep Learning Techniques
The objective of this study is to devise an integrated approach for predicting air quality using image data and subsequently assessing lung disease severity based on Air Quality Index (AQI). The aim is to implement an integrated approach by refining existing techniques to improve accuracy in predicting AQI and lung disease severity.
