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 3026–3050 of 10307 papers

TitleStatusHype
Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction—0
A Data-Driven Approach to Improve 3D Head-Pose Estimation—0
Disentangled Adversarial Transfer Learning for Physiological Biosignals—0
EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning—0
Deep learning-based Visual Measurement Extraction within an Adaptive Digital Twin Framework from Limited Data Using Transfer Learning—0
Deep learning-based variational autoencoder for classification of quantum and classical states of light—0
Disentangled State Space Representations—0
Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning—0
BIRD: Behavior Induction via Representation-structure Distillation—0
Autonomous crater detection on asteroids using a fully-convolutional neural network—0
Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures—0
Disentangling Space and Time in Video with Hierarchical Variational Auto-encoders—0
Deep Learning-Based Transfer Learning for Classification of Cassava Disease—0
AutoML Systems For Medical Imaging—0
A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks—0
EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters—0
Disjoint Label Space Transfer Learning with Common Factorised Space—0
Deep Learning-based Prediction of Stress and Strain Maps in Arterial Walls for Improved Cardiovascular Risk Assessment—0
Deep learning based prediction of Alzheimer's disease from magnetic resonance images—0
Analysis of Multilingual Sequence-to-Sequence speech recognition systems—0
Distant Transfer Learning via Deep Random Walk—0
Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning—0
Deep-Learning-Based Markerless Pose Estimation Systems in Gait Analysis: DeepLabCut Custom Training and the Refinement Function—0
An Analysis of Semantically-Aligned Speech-Text Embeddings—0
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications—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