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 4826–4850 of 10307 papers

TitleStatusHype
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