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 6426–6450 of 10307 papers

TitleStatusHype
Approaching Neural Chinese Word Segmentation as a Low-Resource Machine Translation Task—0
Semantics, Distortion, and Style Matter: Towards Source-free UDA for Panoramic Segmentation—0
Improving the Transferability of Time Series Forecasting with Decomposition Adaptation—0
Improving Transducer-Based Spoken Language Understanding with Self-Conditioned CTC and Knowledge Transfer—0
Improving Transferability of Deep Neural Networks—0
Approaches for enhancing extrapolability in process-based and data-driven models in hydrology—0
Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference—0
Improving Video Model Transfer With Dynamic Representation Learning—0
MuMUR : Multilingual Multimodal Universal Retrieval—0
Semantics Distortion and Style Matter: Towards Source-free UDA for Panoramic Segmentation—0
Applying Transfer Learning To Deep Learned Models For EEG Analysis—0
Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic Resonance Images—0
Applying Transfer Learning for Improving Domain-Specific Search Experience Using Query to Question Similarity—0
A Supervised Machine Learning Model For Imputing Missing Boarding Stops In Smart Card Data—0
IMS' Systems for the IWSLT 2021 Low-Resource Speech Translation Task—0
IMS’ Systems for the IWSLT 2021 Low-Resource Speech Translation Task—0
Towards Inadequately Pre-trained Models in Transfer Learning—0
Applying Knowledge Transfer for Water Body Segmentation in Peru—0
Inapplicable Actions Learning for Knowledge Transfer in Reinforcement Learning—0
Semantic Segmentation of Skin Lesions using a Small Data Set—0
Generalizing Emergent Communication—0
InceptionCapsule: Inception-Resnet and CapsuleNet with self-attention for medical image Classification—0
Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras—0
In-Context Learning Distillation for Efficient Few-Shot Fine-Tuning—0
Explaining Emergent In-Context Learning as Kernel Regression—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