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 4801–4850 of 10307 papers

TitleStatusHype
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
Improving the Transferability of Time Series Forecasting with Decomposition Adaptation—0
Improving Transducer-Based Spoken Language Understanding with Self-Conditioned CTC and Knowledge Transfer—0
Improving Transferability of Deep Neural Networks—0
Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference—0
Improving Video Model Transfer With Dynamic Representation Learning—0
MuMUR : Multilingual Multimodal Universal Retrieval—0
A Supervised Machine Learning Model For Imputing Missing Boarding Stops In Smart Card Data—0
IMS' Systems for the IWSLT 2021 Low-Resource Speech Translation Task—0
IMS’ Systems for the IWSLT 2021 Low-Resource Speech Translation Task—0
Towards Inadequately Pre-trained Models in Transfer Learning—0
Inapplicable Actions Learning for Knowledge Transfer in Reinforcement Learning—0
Generalizing Emergent Communication—0
InceptionCapsule: Inception-Resnet and CapsuleNet with self-attention for medical image Classification—0
Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras—0
In-Context Learning Distillation for Efficient Few-Shot Fine-Tuning—0
Explaining Emergent In-Context Learning as Kernel Regression—0
In-Context Operator Learning for Linear Propagator Models—0
Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddings—0
Incorporating Ensemble and Transfer Learning For An End-To-End Auto-Colorized Image Detection Model—0
Increasing Shape Bias in ImageNet-Trained Networks Using Transfer Learning and Domain-Adversarial Methods—0
Incremental Feature Learning For Infinite Data—0
Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing—0
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation—0
Incremental Learning with Maximum Entropy Regularization: Rethinking Forgetting and Intransigence—0
Incrementally Learning Multiple Diverse Data Domains via Multi-Source Dynamic Expansion Model—0
IndicBART: A Pre-trained Model for Indic Natural Language Generation—0
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors—0
Individual Fairness Through Reweighting and Tuning—0
Text-to-speech for the hearing impaired—0
Indoor Localization Under Limited Measurements: A Cross-Environment Joint Semi-Supervised and Transfer Learning Approach—0
Indoor Positioning via Gradient Boosting Enhanced with Feature Augmentation using Deep Learning—0
Induction of Subgoal Automata for Reinforcement Learning—0
Industrial Federated Learning -- Requirements and System Design—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