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 7301–7325 of 10307 papers

TitleStatusHype
Low-Resolution Face Recognition via Adaptable Instance-Relation Distillation—0
Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation—0
Low-Resource Adaptation of Neural NLP Models—0
Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications—0
Low-Resource Cross-Lingual Summarization through Few-Shot Learning with Large Language Models—0
Low-resource Deep Entity Resolution with Transfer and Active Learning—0
Low-Resource End-to-end Sanskrit TTS using Tacotron2, WaveGlow and Transfer Learning—0
Low-resource Information Extraction with the European Clinical Case Corpus—0
Low-resource Low-footprint Wake-word Detection using Knowledge Distillation—0
An Empirical Study of Language Relatedness for Transfer Learning in Neural Machine Translation—0
Low-resource named entity recognition via multi-source projection: Not quite there yet?—0
Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields—0
Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison—0
Low-Resource Neural Machine Translation for Southern African Languages—0
Low Resource Sequence Tagging using Sentence Reconstruction—0
Low-Resource Transliteration for Roman-Urdu and Urdu Using Transformer-Based Models—0
Low-Resource Vision Challenges for Foundation Models—0
Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches—0
Low-supervision urgency detection and transfer in short crisis messages—0
Low Tensor-Rank Adaptation of Kolmogorov--Arnold Networks—0
An empirical investigation into audio pipeline approaches for classifying bird species—0
LRDB: LSTM Raw data DNA Base-caller based on long-short term models in an active learning environment—0
LS-DYNA Machine Learning-based Multiscale Method for Nonlinear Modeling of Short Fiber-Reinforced Composites—0
Småprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learning—0
An Empirical Evaluation of Text Representation Schemes on Multilingual Social Web to Filter the Textual Aggression—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