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 4301–4325 of 10307 papers

TitleStatusHype
Enabling Continual Learning in Neural Networks with Meta Learning—0
Enabling Asymmetric Knowledge Transfer in Multi-Task Learning with Self-Auxiliaries—0
Channel-wise pruning of neural networks with tapering resource constraint—0
Emulation Learning for Neuromimetic Systems—0
Channel Scaling: A Scale-and-Select Approach for Transfer Learning—0
Change your singer: a transfer learning generative adversarial framework for song to song conversion—0
A Petri Dish for Histopathology Image Analysis—0
A Permutation-Invariant Representation of Neural Networks with Neuron Embeddings—0
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)—0
Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecture—0
A Pathology-Based Machine Learning Method to Assist in Epithelial Dysplasia Diagnosis—0
Empowering Agricultural Insights: RiceLeafBD - A Novel Dataset and Optimal Model Selection for Rice Leaf Disease Diagnosis through Transfer Learning Technique—0
Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification—0
Challenges in including extra-linguistic context in pre-trained language models—0
Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings—0
Employing High-Dimensional RIS Information for RIS-aided Localization Systems—0
Challenges for cognitive decoding using deep learning methods—0
A PAC-Bayesian bound for Lifelong Learning—0
Employing Federated Learning for Training Autonomous HVAC Systems—0
Empirical study of pretrained multilingual language models for zero-shot cross-lingual knowledge transfer in generation—0
Empirically Measuring Transfer Distance for System Design and Operation—0
ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research—0
Empirical Gaussian priors for cross-lingual transfer learning—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Empirical Evaluation of Knowledge Distillation from Transformers to Subquadratic Language Models—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