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 4851–4875 of 10307 papers

TitleStatusHype
Understanding and Improving Transfer Learning of Deep Models via Neural Collapse—0
ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight ConditionsCode0
Generalization Bounds for Few-Shot Transfer Learning with Pretrained Classifiers—0
Federated Learning -- Methods, Applications and beyond—0
Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning—0
Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification taskCode0
Object detection-based inspection of power line insulators: Incipient fault detection in the low data-regime—0
HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processesCode0
On the Role of Parallel Data in Cross-lingual Transfer Learning—0
A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach—0
An Information-Theoretic Approach to Transferability in Task Transfer Learning—0
Using Machine Learning to Determine Morphologies of z<1 AGN Host Galaxies in the Hyper Suprime-Cam Wide SurveyCode0
Improving the Generalizability of Text-Based Emotion Detection by Leveraging Transformers with Psycholinguistic Features—0
COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images—0
Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling—0
Memory-efficient NLLB-200: Language-specific Expert Pruning of a Massively Multilingual Machine Translation Model—0
Building Height Prediction with Instance Segmentation—0
Leveraging Road Area Semantic Segmentation with Auxiliary Steering Task—0
PoE: a Panel of Experts for Generalized Automatic Dialogue Assessment—0
Graph Neural Network based Child Activity Recognition—0
Plansformer: Generating Symbolic Plans using Transformers—0
Swing Distillation: A Privacy-Preserving Knowledge Distillation Framework—0
Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods—0
Penalised regression with multiple sources of prior effectsCode0
Rethinking Cooking State Recognition with Vision TransformersCode0
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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