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 5076–5100 of 10307 papers

TitleStatusHype
Label Representations in Modeling Classification as Text Generation—0
LABOR-LLM: Language-Based Occupational Representations with Large Language Models—0
LAC: Latent Action Composition for Skeleton-based Action Segmentation—0
LAC - Latent Action Composition for Skeleton-based Action Segmentation—0
LAGUNA: LAnguage Guided UNsupervised Adaptation with structured spaces—0
LAMA-Net: Unsupervised Domain Adaptation via Latent Alignment and Manifold Learning for RUL Prediction—0
Land Cover Semantic Segmentation Using ResUNet—0
Landmark-Aware and Part-based Ensemble Transfer Learning Network for Facial Expression Recognition from Static images—0
Land Use Prediction using Electro-Optical to SAR Few-Shot Transfer Learning—0
Language Branch Gated Multilingual Neural Machine Translation—0
Language Chameleon: Transformation analysis between languages using Cross-lingual Post-training based on Pre-trained language models—0
Language Discrimination and Transfer Learning for Similar Languages: Experiments with Feature Combinations and Adaptation—0
Language Graph Distillation for Low-Resource Machine Translation—0
Language Identification of Hindi-English tweets using code-mixed BERT—0
Language Identification with Deep Bottleneck Features—0
Language-independent Cross-lingual Contextual Representations—0
Language Independent Sentiment Analysis with Sentiment-Specific Word Embeddings—0
Language-Informed Transfer Learning for Embodied Household Activities—0
Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis—0
Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Syntactic Task Analysis—0
Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Task Analysis—0
Language model integration based on memory control for sequence to sequence speech recognition—0
Language Model is All You Need: Natural Language Understanding as Question Answering—0
Language Model Pretraining and Transfer Learning for Very Low Resource Languages—0
Large Language Models are not Models of Natural Language: they are Corpus Models—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