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 7851–7875 of 10307 papers

TitleStatusHype
Multilingual Dyadic Interaction Corpus NoXi+J: Toward Understanding Asian-European Non-verbal Cultural Characteristics and their Influences on Engagement—0
Multilingual End-to-End Speech Translation—0
Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning—0
Speaker Generation—0
Speaking style adaptation in Text-To-Speech synthesis using Sequence-to-sequence models with attention—0
All About Knowledge Graphs for Actions—0
"A Little is Enough": Few-Shot Quality Estimation based Corpus Filtering improves Machine Translation—0
Multilingual Neural Machine Translation—0
SPECI: Skill Prompts based Hierarchical Continual Imitation Learning for Robot Manipulation—0
Multi-lingual neural title generation for e-Commerce browse pages—0
The Creative Frontier of Generative AI: Managing the Novelty-Usefulness Tradeoff—0
SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization—0
Multilingual Offensive Language Identification for Low-resource Languages—0
Multilingual Pre-Trained Transformers and Convolutional NN Classification Models for Technical Domain Identification—0
Multilingual Projection for Parsing Truly Low-Resource Languages—0
Multilingual Prosody Transfer: Comparing Supervised & Transfer Learning—0
SpectFormer: Frequency and Attention is what you need in a Vision Transformer—0
Accumulating Knowledge for Lifelong Online Learning—0
Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling—0
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes—0
Multilingual Speech Translation from Efficient Finetuning of Pretrained Models—0
Multilingual Training of Crosslingual Word Embeddings—0
Multilingual Transfer Learning for Children Automatic Speech Recognition—0
Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling—0
Multilingual Transfer Learning for QA Using Translation as Data Augmentation—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