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 1976–2000 of 10307 papers

TitleStatusHype
Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection—0
Comparison of self-supervised in-domain and supervised out-domain transfer learning for bird species recognition—0
Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection—0
Comparison of semi-supervised learning methods for High Content Screening quality control—0
Comparison of Transfer Learning based Additive Manufacturing Models via A Case Study—0
Compiler Provenance Recovery for Multi-CPU Architectures Using a Centrifuge Mechanism—0
Monocular Cyclist Detection with Convolutional Neural Networks—0
The (In)Effectiveness of Intermediate Task Training For Domain Adaptation and Cross-Lingual Transfer Learning—0
A Sequence Matching Network for Polyphonic Sound Event Localization and Detection—0
Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition—0
A physics-based domain adaptation framework for modelling and forecasting building energy systems—0
A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition—0
Channel-wise pruning of neural networks with tapering resource constraint—0
Composable Sparse Fine-Tuning for Cross-Lingual Transfer—0
Composing Task-Agnostic Policies with Deep Reinforcement Learning—0
Time-Frequency Analysis based Deep Interference Classification for Frequency Hopping System—0
A Petri Dish for Histopathology Image Analysis—0
Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks—0
Compositional Zero-Shot Domain Transfer with Text-to-Text Models—0
Channel Scaling: A Scale-and-Select Approach for Transfer Learning—0
Change your singer: a transfer learning generative adversarial framework for song to song conversion—0
A Permutation-Invariant Representation of Neural Networks with Neuron Embeddings—0
Comprehensive performance comparison among different types of features in data-driven battery state of health estimation—0
Action Recognition for American Sign Language—0
Assessing the Performance of Analog Training for Transfer Learning—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