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 6576–6600 of 10307 papers

TitleStatusHype
Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning—0
Sensing Urban Land-Use Patterns By Integrating Google Tensorflow And Scene-Classification Models—0
Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil—0
Inverse design with conditional cascaded diffusion models—0
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics—0
Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption—0
Investigating and Exploiting Image Resolution for Transfer Learning-based Skin Lesion Classification—0
Investigating Continual Pretraining in Large Language Models: Insights and Implications—0
Investigating Continuous Learning in Spiking Neural Networks—0
Investigating GANsformer: A Replication Study of a State-of-the-Art Image Generation Model—0
Sensor2Text: Enabling Natural Language Interactions for Daily Activity Tracking Using Wearable Sensors—0
Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem—0
Investigating Multilingual NMT Representations at Scale—0
Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation—0
A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications—0
Investigating Relative Performance of Transfer and Meta Learning—0
Test-time Adaptation for Real Image Denoising via Meta-transfer Learning—0
Investigating self-supervised, weakly supervised and fully supervised training approaches for multi-domain automatic speech recognition: a study on Bangladeshi Bangla—0
Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications—0
Investigating the Impact of Weight Sharing Decisions on Knowledge Transfer in Continual Learning—0
Investigating the potential of Sparse Mixtures-of-Experts for multi-domain neural machine translation—0
Investigating the role of model-based learning in exploration and transfer—0
A Petri Dish for Histopathology Image Analysis—0
Sentence encoding for Dialogue Act classification—0
Sentence-Level Propaganda Detection in News Articles with Transfer Learning and BERT-BiLSTM-Capsule Model—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