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 871–880 of 10307 papers

Title	Date	Tasks	Status	Hype
Adversarial Masking for Self-Supervised Learning	Jan 31, 2022	Representation LearningSelf-Supervised Learning	CodeCode Available	1
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception	Aug 19, 2021	Action RecognitionImage Quality Assessment	CodeCode Available	1
Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation	Aug 23, 2023	Network PruningTransfer Learning	CodeCode Available	1
LEAD: Learning Decomposition for Source-free Universal Domain Adaptation	Mar 6, 2024	Domain AdaptationTransfer Learning	CodeCode Available	1
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling	Nov 7, 2022	DisentanglementDomain Generalization	CodeCode Available	1
Learning context-aware structural representations to predict antigen and antibody binding interfaces	Apr 22, 2020	Antibody-antigen binding predictionTransfer Learning	CodeCode Available	1
BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity	Jun 18, 2024	Contrastive LearningLanguage Modelling	CodeCode Available	1
Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions	Dec 10, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Learning Graph Embeddings for Compositional Zero-shot Learning	Feb 3, 2021	Compositional Zero-Shot LearningGraph Embedding	CodeCode Available	1
MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution	Sep 27, 2019	GPUInstance Segmentation	CodeCode Available	1

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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