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 4201–4250 of 10307 papers

TitleStatusHype
Self-supervised Model Based on Masked Autoencoders Advance CT Scans Classification—0
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution DataCode0
TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot LearningCode0
The Fast and Accurate Approach to Detection and Segmentation of Melanoma Skin Cancer using Fine-tuned Yolov3 and SegNet Based on Deep Transfer Learning—0
Fast Hierarchical Learning for Few-Shot Object Detection—0
A Survey on Heterogeneous Federated Learning—0
Association Graph Learning for Multi-Task Classification with Category ShiftsCode1
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects EstimationCode1
The effect of variable labels on deep learning models trained to predict breast density—0
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-ExpertsCode1
Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model EmulationCode0
Training Deep Learning Algorithms on Synthetic Forest Images for Tree DetectionCode1
Evaluating the Performance of StyleGAN2-ADA on Medical Images—0
SpaceQA: Answering Questions about the Design of Space Missions and Space Craft ConceptsCode0
Explainable AI based Glaucoma Detection using Transfer Learning and LIME—0
Unsupervised Neural Stylistic Text Generation using Transfer learning and Adapters—0
Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization—0
Gastrointestinal Disorder Detection with a Transformer Based Approach—0
Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data—0
Matching Text and Audio Embeddings: Exploring Transfer-learning Strategies for Language-based Audio Retrieval—0
Biological neurons act as generalization filters in reservoir computing—0
Data-driven Approaches to Surrogate Machine Learning Model Development—0
Content-Based Search for Deep Generative ModelsCode2
TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning—0
Exploring Effective Knowledge Transfer for Few-shot Object DetectionCode1
On Neural Consolidation for Transfer in Reinforcement Learning—0
ImpressLearn: Continual Learning via Combined Task Impressions—0
Star-Graph Multimodal Matching Component Analysis for Data Fusion and Transfer Learning—0
Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts—0
Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search—0
Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks—0
Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond—0
HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization—0
The (In)Effectiveness of Intermediate Task Training For Domain Adaptation and Cross-Lingual Transfer Learning—0
An Analysis of RF Transfer Learning Behavior Using Synthetic Data—0
On The Effects Of Data Normalisation For Domain Adaptation On EEG Data—0
Under the Cover Infant Pose Estimation using Multimodal DataCode0
Towards a Unified View on Visual Parameter-Efficient Transfer LearningCode1
Visual Prompt Tuning for Generative Transfer LearningCode1
Unbiased Scene Graph Generation using Predicate Similarities—0
PCONet: A Convolutional Neural Network Architecture to Detect Polycystic Ovary Syndrome (PCOS) from Ovarian Ultrasound Images—0
DARE: A large-scale handwritten date recognition system—0
Spectral Augmentation for Self-Supervised Learning on GraphsCode1
Exploring Multimodal Features and Fusion Strategies for Analyzing Disaster Tweets—0
Knowledge Transfer with Visual Prompt in multi-modal Dialogue Understanding and Generation—0
Can Attention-based Transformers Explain or Interpret Cyberbullying Detection?—0
Machine Translation for a Very Low-Resource Language - Layer Freezing Approach on Transfer Learning—0
Detection of Negative Campaign in Israeli Municipal ElectionsCode0
Transfer Learning Improves French Cross-Domain Dialect Identification: NRC @ VarDial 2022—0
Multiple Pivot Languages and Strategic Decoder Initialization Helps Neural Machine 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