Transfer Learning

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 )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 731–740 of 10307 papers

Title	Date	Tasks	Status	Hype	Score
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning	Oct 8, 2020	Reinforcement Learning (RL)Transfer Learning	CodeCode Available	1	5
FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition	May 27, 2023	image-classificationImage Classification	CodeCode Available	1	5
ARWKV: Pretrain is not what we need, an RNN-Attention-Based Language Model Born from Transformer	Jan 26, 2025	Language ModelingLanguage Modelling	CodeCode Available	1	5
Fine-grained Prompt Tuning: A Parameter and Memory Efficient Transfer Learning Method for High-resolution Medical Image Classification	Mar 12, 2024	image-classificationImage Classification	CodeCode Available	1	5
Deeply Coupled Cross-Modal Prompt Learning	May 29, 2023	Domain AdaptationFew-Shot Learning	CodeCode Available	1	5
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement Learning	Aug 5, 2021	Audio captioningDecoder	CodeCode Available	1	5
A Deep Learning-Based Supervised Transfer Learning Framework for DOA Estimation with Array Imperfections	Apr 18, 2025	Deep LearningTransfer Learning	CodeCode Available	1	5
CEM500K – A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning	Dec 11, 2020	Electron Microscopy Image SegmentationSegmentation	CodeCode Available	1	5
An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations	May 15, 2023	Automated Theorem ProvingTransfer Learning	CodeCode Available	1	5
Deep Recurrent Neural Networks for ECG Signal Denoising	Jul 30, 2018	DenoisingECG Denoising	CodeCode Available	1	5

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All datasets Office-Home 100 sleep nights of 8 caregivers BanglaLekha Isolated Dataset COCO70 KITTI Object Tracking Evaluation 2012 Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	APCLIP	Accuracy	84.2	—	Unverified
2	DFA-ENT	Accuracy	69.2	—	Unverified
3	DFA-SAFN	Accuracy	69.1	—	Unverified
4	EasyTL	Accuracy	63.3	—	Unverified
5	MEDA	Accuracy	60.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CNN	10-20% Mask PSNR	3.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Chatterjee, Dutta et al.[1]	Accuracy	96.12	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Co-Tuning	Accuracy	85.65	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Physical Access	EER	5.74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	riadd.aucmedi	AUROC	0.95	—	Unverified