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 6451–6500 of 10307 papers

TitleStatusHype
Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data—0
Improving Feature Extraction from Histopathological Images Through A Fine-tuning ImageNet Model—0
Improving filling level classification with adversarial training—0
Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning—0
Improving generalization in reinforcement learning through forked agents—0
Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving—0
Improving Hyperparameter Optimization by Planning Ahead—0
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning—0
Improving Japanese semantic-role-labeling performance with transfer learning as case for limited resources of tagged corpora on aggregated language—0
Improving Knowledge Distillation in Transfer Learning with Layer-wise Learning Rates—0
Improving Language and Modality Transfer in Translation by Character-level Modeling—0
Improving Local Air Quality Predictions Using Transfer Learning on Satellite Data and Graph Neural Networks—0
Improving Location-based Thermal Emission Side-Channel Analysis Using Iterative Transfer Learning—0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning—0
Improving Machine Translation of Educational Content via Crowdsourcing—0
Improving Mammography Malignancy Segmentation by Designing the Training Process—0
Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study—0
Improving Multi-fidelity Optimization with a Recurring Learning Rate for Hyperparameter Tuning—0
Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network—0
Improving Natural-Language-based Audio Retrieval with Transfer Learning and Audio & Text Augmentations—0
Improving Neural Machine Translation by Achieving Knowledge Transfer with Sentence Alignment Learning—0
Improving Neural Machine Translation for Sanskrit-English—0
Improving Neural Machine Translation of Indigenous Languages with Multilingual Transfer Learning—0
Improving neural network predictions of material properties with limited data using transfer learning—0
Improving performance and inference on audio classification tasks using capsule networks—0
Improving performance of CNN to predict likelihood of COVID-19 using chest X-ray images with preprocessing algorithms—0
Improving physics-informed neural networks with meta-learned optimization—0
Improving plant disease classification by adaptive minimal ensembling—0
Improving Polish to English Neural Machine Translation with Transfer Learning: Effects of Data Volume and Language Similarity—0
Improving Polyphonic Sound Event Detection on Multichannel Recordings with the Sørensen-Dice Coefficient Loss and Transfer Learning—0
Improving Pretrained YAMNet for Enhanced Speech Command Detection via Transfer Learning—0
Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma—0
Improving Prostate Cancer Detection with Breast Histopathology Images—0
Improving Quality Control Of MRI Images Using Synthetic Motion Data—0
Improving Relevance Prediction with Transfer Learning in Large-scale Retrieval Systems—0
Improving Satellite Imagery Masking using Multi-task and Transfer Learning—0
Improving Sentence Boundary Detection for Spoken Language Transcripts—0
Improving Signer Independent Sign Language Recognition for Low Resource Languages—0
Improving Similar Language Translation With Transfer Learning—0
Improving Source-Free Target Adaptation with Vision Transformers Leveraging Domain Representation Images—0
Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning—0
Improving speaker turn embedding by crossmodal transfer learning from face embedding—0
Improving speech emotion recognition via Transformer-based Predictive Coding through transfer learning—0
Improving speech recognition models with small samples for air traffic control systems—0
Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning—0
Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task—0
Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text—0
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation—0
Improving the Generalizability of Text-Based Emotion Detection by Leveraging Transformers with Psycholinguistic Features—0
Improving the Language Model for Low-Resource ASR with Online Text Corpora—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