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 6076–6100 of 10307 papers

TitleStatusHype
Transfer learning using deep neural networks for Ear Presentation Attack Detection: New Database for PADCode0
Pareto Domain AdaptationCode0
Best Arm Identification under Additive Transfer Bandits—0
ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake ImagesCode0
Flexible Option LearningCode0
CSG0: Continual Urban Scene Generation with Zero Forgetting—0
Prototypical Model with Novel Information-theoretic Loss Function for Generalized Zero Shot Learning—0
Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks—0
Transfer learning to improve streamflow forecasts in data sparse regions—0
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks—0
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning—0
Overcome Anterograde Forgetting with Cycled Memory Networks—0
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning—0
Learning Curves for Continual Learning in Neural Networks: Self-Knowledge Transfer and Forgetting—0
Fast Data-Driven Adaptation of Radar Detection via Meta-Learning—0
Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks—0
Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors—0
Variational Continual Bayesian Meta-Learning—0
Unsupervised Representation Transfer for Small Networks: I Believe I Can Distill On-the-Fly—0
Wiki to Automotive: Understanding the Distribution Shift and its impact on Named Entity Recognition—0
以遷移學習改善深度神經網路模型於中文歌詞情緒辨識 (Using Transfer Learning to Improve Deep Neural Networks for Lyrics Emotion Recognition in Chinese)—0
Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism—0
On the Transferability of Massively Multilingual Pretrained Models in the Pretext of the Indo-Aryan and Tibeto-Burman Languages—0
Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems—0
NLP in the DH pipeline: Transfer-learning to a Chronolect—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