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 4151–4175 of 10307 papers

TitleStatusHype
Global Extreme Heat Forecasting Using Neural Weather Models—0
Global Flood Prediction: a Multimodal Machine Learning Approach—0
Data-Efficient Methods for Dialogue Systems—0
Data Efficient Lithography Modeling with Transfer Learning and Active Data Selection—0
Automated Blood Cell Detection and Counting via Deep Learning for Microfluidic Point-of-Care Medical Devices—0
Data-Efficient Information Extraction from Form-Like Documents—0
A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification—0
A knowledge transfer model for COVID-19 predicting and non-pharmaceutical intervention simulation—0
Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data—0
Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks—0
Automated Audio Captioning using Transfer Learning and Reconstruction Latent Space Similarity Regularization—0
Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning—0
Adaptive Target Localization under Uncertainty using Multi-Agent Deep Reinforcement Learning with Knowledge Transfer—0
Global Estimation of Subsurface Eddy Kinetic Energy of Mesoscale Eddies Using a Multiple-input Residual Neural Network—0
Global Weighted Average Pooling Bridges Pixel-level Localization and Image-level Classification—0
Data-Efficient Challenges in Visual Inductive Priors: A Retrospective—0
3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: State-of-the-art results on affordable hardware—0
GLID: Pre-training a Generalist Encoder-Decoder Vision Model—0
Data-Driven Transferred Energy Management Strategy for Hybrid Electric Vehicles via Deep Reinforcement Learning—0
Data-Driven Transfer Learning Framework for Estimating Turning Movement Counts—0
AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction—0
Data-driven tool wear prediction in milling, based on a process-integrated single-sensor approach—0
Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning—0
Amortized Network Intervention to Steer the Excitatory Point Processes—0
Adaptive Sparse Transformer for Multilingual Translation—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