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 5526–5550 of 10307 papers

TitleStatusHype
A Corpus for Commonsense Inference in Story Cloze Test—0
Cross-lingual and Cross-domain Transfer Learning for Automatic Term Extraction from Low Resource Data—0
Negation Detection in Dutch Spoken Human-Computer Conversations—0
Supervised Denoising of Diffusion-Weighted Magnetic Resonance Images Using a Convolutional Neural Network and Transfer Learning—0
CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection—0
Evaluating Gaussian Grasp Maps for Generative Grasping Models—0
Medical Crossing: a Cross-lingual Evaluation of Clinical Entity LinkingCode0
Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text SummarizationCode0
Improving Signer Independent Sign Language Recognition for Low Resource Languages—0
Text-to-Speech for Under-Resourced Languages: Phoneme Mapping and Source Language Selection in Transfer Learning—0
Exploring Transfer Learning for Urdu Speech Synthesis—0
Named Entity Recognition in Estonian 19th Century Parish Court RecordsCode0
SpecNFS: A Challenge Dataset Towards Extracting Formal Models from Natural Language SpecificationsCode0
Extracting and Analysing Metaphors in Migration Media Discourse: towards a Metaphor Annotation SchemeCode0
The elements of flexibility for task-performing systems—0
A Dataset of Offensive Language in Kosovo Social Media—0
DTW at Qur’an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
Multilingual Transfer Learning for Children Automatic Speech Recognition—0
Domain Mismatch Doesn’t Always Prevent Cross-lingual Transfer Learning—0
Challenging the Assumption of Structure-based embeddings in Few- and Zero-shot Knowledge Graph CompletionCode0
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian—0
Resources and Experiments on Sentiment Classification for Georgian—0
A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation—0
Bazinga! A Dataset for Multi-Party Dialogues Structuring—0
Embeddings models for Buddhist Sanskrit—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