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 3676–3700 of 10307 papers

TitleStatusHype
Event Camera Data Pre-training—0
Feature-Supervised Action Modality Transfer—0
Event Extraction in Basque: Typologically motivated Cross-Lingual Transfer-Learning Analysis—0
Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring—0
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning—0
A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems—0
Everything old is new again: A multi-view learning approach to learning using privileged information and distillation—0
Deep CNNs for large scale species classification—0
Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification-—0
Feature Transfer Learning for Deep Face Recognition with Under-Represented Data—0
Deep Clustering of Remote Sensing Scenes through Heterogeneous Transfer Learning—0
Evolutionary Algorithms Approach For Search Based On Semantic Document Similarity—0
Evolutionary Algorithms in the Light of SGD: Limit Equivalence, Minima Flatness, and Transfer Learning—0
Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning—0
Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains—0
Automatic Audio Captioning using Attention weighted Event based Embeddings—0
Evolution of ReID: From Early Methods to LLM Integration—0
Evolution of transfer learning in natural language processing—0
Evolving Image Compositions for Feature Representation Learning—0
Feature Interaction Fusion Self-Distillation Network For CTR Prediction—0
A multi-objective perspective on jointly tuning hardware and hyperparameters—0
Adaptive Transfer Learning of Multi-View Time Series Classification—0
DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices—0
Deep Bag-of-Sub-Emotions for Depression Detection in Social Media—0
Automatically Score Tissue Images Like a Pathologist by Transfer Learning—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