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 9476–9500 of 10307 papers

TitleStatusHype
ConvNets vs. Transformers: Whose Visual Representations are More Transferable?—0
Differentially Private Prototypes for Imbalanced Transfer Learning—0
Ladder Bottom-up Convolutional Bidirectional Variational Autoencoder for Image Translation of Dotted Arabic Expiration Dates—0
Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection—0
Convolutional Drift Networks for Video Classification—0
Convolutional Gated MLP: Combining Convolutions & gMLP—0
Convolutional-network models to predict wall-bounded turbulence from wall quantities—0
Convolutional Neural Network and Transfer Learning for High Impedance Fault Detection—0
Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example—0
Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism—0
Convolutional neural network for Lyman break galaxies classification and redshift regression in DESI (Dark Energy Spectroscopic Instrument)—0
Convolutional Neural Network for Universal Sentence Embeddings—0
Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson's Disease from Speech in Three Different Languages—0
Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks—0
Beyond Similarity: A Gradient-based Graph Method for Instruction Tuning Data Selection—0
Recent Advancements and Challenges of Turkic Central Asian Language Processing—0
Convolutional Neural Networks for Breast Cancer Screening: Transfer Learning with Exponential Decay—0
Convolutional Neural Networks for Classifying Melanoma Images—0
Convolutional Neural Networks for Financial Text Regression—0
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?—0
Recent Advances in Optimal Transport for Machine Learning—0
Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams—0
Convolutional Neural Networks Towards Facial Skin Lesions Detection—0
Recent Advances of Foundation Language Models-based Continual Learning: A Survey—0
Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer—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