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 5151–5175 of 10307 papers

TitleStatusHype
Unbiased Scene Graph Generation using Predicate Similarities—0
Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond—0
The (In)Effectiveness of Intermediate Task Training For Domain Adaptation and Cross-Lingual Transfer Learning—0
On The Effects Of Data Normalisation For Domain Adaptation On EEG Data—0
HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization—0
Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks—0
An Analysis of RF Transfer Learning Behavior Using Synthetic Data—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
Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation—0
Can Attention-based Transformers Explain or Interpret Cyberbullying Detection?—0
Argument Novelty and Validity Assessment via Multitask and Transfer Learning—0
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model CompressionCode0
MECI: A Multilingual Dataset for Event Causality IdentificationCode0
Addressing Asymmetry in Multilingual Neural Machine Translation with Fuzzy Task Clustering—0
How to Parse a Creole: When Martinican Creole Meets French—0
Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at Each Single-Hop?Code0
Knowledge Transfer with Visual Prompt in multi-modal Dialogue Understanding and Generation—0
基于知识迁移的情感-原因对抽取(Emotion-Cause Pair Extraction Based on Knowledge-Transfer)—0
Analyzing BERT Cross-lingual Transfer Capabilities in Continual Sequence LabelingCode0
Cluster-aware Pseudo-Labeling for Supervised Open Relation ExtractionCode0
Language Branch Gated Multilingual Neural Machine Translation—0
Machine Translation for a Very Low-Resource Language - Layer Freezing Approach on Transfer Learning—0
Detection of Negative Campaign in Israeli Municipal ElectionsCode0
Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun EntailmentCode0
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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