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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 401–425 of 10307 papers

TitleStatusHype
Deconfounded Representation Similarity for Comparison of Neural NetworksCode1
Decoupled Multimodal Distilling for Emotion RecognitionCode1
Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic DataCode1
A Systematic Benchmarking Analysis of Transfer Learning for Medical Image AnalysisCode1
Deep comparisons of Neural Networks from the EEGNet familyCode1
Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer LearningCode1
Abstractive Summarization of Spoken and Written Instructions with BERTCode1
A Token is Worth over 1,000 Tokens: Efficient Knowledge Distillation through Low-Rank CloneCode1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer LearningCode1
Deep Fast Vision: A Python Library for Accelerated Deep Transfer Learning Vision PrototypingCode1
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft PromptsCode1
A transfer learning based approach for pronunciation scoringCode1
Deep Image Harmonization by Bridging the Reality GapCode1
DeepKD: A Deeply Decoupled and Denoised Knowledge Distillation TrainerCode1
Adaptive Transfer Learning on Graph Neural NetworksCode1
AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder NetworksCode1
An Empirical Study on Cross-X Transfer for Legal Judgment PredictionCode1
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XLCode1
Audio-based Near-Duplicate Video Retrieval with Audio Similarity LearningCode1
Audio Embeddings as Teachers for Music ClassificationCode1
Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samplesCode1
An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature SelectionCode1
Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake modelsCode1
Masking meets Supervision: A Strong Learning AllianceCode1
An Empirical Analysis of Image-Based Learning Techniques for Malware ClassificationCode1
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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