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 8701–8725 of 10307 papers

TitleStatusHype
Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding TasksCode0
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword RepresentationsCode0
Adaptive Growth: Real-time CNN Layer ExpansionCode0
Adaptive Meta-Domain Transfer Learning (AMDTL): A Novel Approach for Knowledge Transfer in AICode0
Adaptive Multi-Task Transfer Learning for Chinese Word Segmentation in Medical TextCode0
Adaptive Prompt Learning with Distilled Connective Knowledge for Implicit Discourse Relation RecognitionCode0
Task and Domain Adaptive Reinforcement Learning for Robot ControlCode0
Adaptive Transfer Clustering: A Unified FrameworkCode0
Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment ClassificationCode0
AdaRank: Disagreement Based Module Rank Prediction for Low-rank AdaptationCode0
AdaTriplet-RA: Domain Matching via Adaptive Triplet and Reinforced Attention for Unsupervised Domain AdaptationCode0
ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake ImagesCode0
Addressee and Response Selection for Multilingual ConversationCode0
A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-RaysCode0
A Deep Learning Method for Comparing Bayesian Hierarchical ModelsCode0
AdeNet: Deep learning architecture that identifies damaged electrical insulators in power linesCode0
A Diffusion Model and Knowledge Distillation Framework for Robust Coral Detection in Complex Underwater EnvironmentsCode0
Adjustment for Confounding using Pre-Trained RepresentationsCode0
A domain adaptation neural network for digital twin-supported fault diagnosisCode0
AdPE: Adversarial Positional Embeddings for Pretraining Vision Transformers via MAE+Code0
Advanced Knowledge Transfer: Refined Feature Distillation for Zero-Shot Quantization in Edge ComputingCode0
Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation ModelsCode0
Advances in deep learning methods for pavement surface crack detection and identification with visible light visual imagesCode0
Advancing Adversarial Suffix Transfer Learning on Aligned Large Language ModelsCode0
Advancing Compressed Video Action Recognition through Progressive Knowledge DistillationCode0
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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