SOTAVerified

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

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Showing 451–475 of 10307 papers

TitleStatusHype
AugFL: Augmenting Federated Learning with Pretrained ModelsCode0
Generalized Diffusion Detector: Mining Robust Features from Diffusion Models for Domain-Generalized DetectionCode1
Conditional Electrocardiogram Generation Using Hierarchical Variational Autoencoders—0
Diagnosis of Patients with Viral, Bacterial, and Non-Pneumonia Based on Chest X-Ray Images Using Convolutional Neural Networks—0
Hyperspectral Image Restoration and Super-resolution with Physics-Aware Deep Learning for Biomedical Applications—0
A General Neural Network Potential for Energetic Materials with C, H, N, and O elementsCode1
An Efficient Approach to Detecting Lung Nodules Using Swin Transformer—0
A Zero-Shot Learning Approach for Ephemeral Gully Detection from Remote Sensing using Vision Language Models—0
A Transfer Framework for Enhancing Temporal Graph Learning in Data-Scarce Settings—0
Asynchronous Personalized Federated Learning through Global Memorization—0
Rapid morphology characterization of two-dimensional TMDs and lateral heterostructures based on deep learningCode0
Towards Understanding the Benefit of Multitask Representation Learning in Decision Process—0
Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders—0
MIGE: A Unified Framework for Multimodal Instruction-Based Image Generation and EditingCode2
ECLeKTic: a Novel Challenge Set for Evaluation of Cross-Lingual Knowledge Transfer—0
Optimal Transfer Learning for Missing Not-at-Random Matrix Completion—0
Foundation-Model-Boosted Multimodal Learning for fMRI-based Neuropathic Pain Drug Response PredictionCode0
RuCCoD: Towards Automated ICD Coding in RussianCode0
Exploring Open-world Continual Learning with Knowns-Unknowns Knowledge TransferCode0
Transfer Learning in Latent Contextual Bandits with Covariate Shift Through Causal TransportabilityCode0
Long-Context Inference with Retrieval-Augmented Speculative DecodingCode1
Deep Convolutional Neural Networks for Palm Fruit Maturity ClassificationCode0
Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage PerspectiveCode0
GraphBridge: Towards Arbitrary Transfer Learning in GNNsCode0
Deep Learning-Based Transfer Learning for Classification of Cassava Disease—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified