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 9651–9675 of 10307 papers

TitleStatusHype
Cross-lingual dependency parsing for closely related languages - Helsinki's submission to VarDial 2017—0
Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement—0
Cross-lingual Fine-tuning for Abstractive Arabic Text Summarization—0
Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop—0
Cross-lingual hate speech detection based on multilingual domain-specific word embeddings—0
Cross-Lingual Image Caption Generation—0
Reducing Intraspecies and Interspecies Covariate Shift in Traumatic Brain Injury EEG of Humans and Mice Using Transfer Euclidean Alignment—0
Cross-lingual Knowledge Transfer and Iterative Pseudo-labeling for Low-Resource Speech Recognition with Transducers—0
Redundancy Analysis and Mitigation for Machine Learning-Based Process Monitoring of Additive Manufacturing—0
Cross-lingual Knowledge Transfer via Distillation for Multilingual Information Retrieval—0
RedWhale: An Adapted Korean LLM Through Efficient Continual Pretraining—0
Cross-lingual Lifelong Learning—0
Reed at SemEval-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis—0
Re-examining Routing Networks for Multi-task Learning—0
Reference Resolution and Context Change in Multimodal Situated Dialogue for Exploring Data Visualizations—0
Cross-lingual Opinion Analysis via Negative Transfer Detection—0
Cross-Lingual Parser Selection for Low-Resource Languages—0
Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation—0
Cross-lingual Semantic Role Labelling with the ValPaL Database Knowledge—0
The Master Key Filters Hypothesis: Deep Filters Are General—0
Refined Continuous Control of DDPG Actors via Parametrised Activation—0
Cross-lingual Structure Transfer for Zero-resource Event Extraction—0
Cross-Lingual Syntactically Informed Distributed Word Representations—0
Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis—0
Cross-lingual Transfer Learning and Multitask Learning for Capturing Multiword Expressions—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