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 8376–8400 of 10307 papers

TitleStatusHype
On the safety of vulnerable road users by cyclist orientation detection using Deep Learning—0
On the Steganographic Capacity of Selected Learning Models—0
Affordance Labeling and Exploration: A Manifold-Based Approach—0
On the Theory of Transfer Learning: The Importance of Task Diversity—0
On the topology and geometry of population-based SHM—0
On The Transferability of Deep-Q Networks—0
On the Transferability of Massively Multilingual Pretrained Models in the Pretext of the Indo-Aryan and Tibeto-Burman Languages—0
On the Transferability of Representations in Neural Networks Between Datasets and Tasks—0
On the Transferability of VAE Embeddings using Relational Knowledge with Semi-Supervision—0
Affect inTweets: A Transfer Learning Approach—0
On the Transfer of Knowledge in Quantum Algorithms—0
On the universality of neural encodings in CNNs—0
Analysis of Knowledge Transfer in Kernel Regime—0
On the Usability of Transformers-based models for a French Question-Answering task—0
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets—0
Structural Similarity for Improved Transfer in Reinforcement Learning—0
On the Use of Power Amplifier Nonlinearity Quotient to Improve Radio Frequency Fingerprint Identification in Time-Varying Channels—0
On the Value of Target Data in Transfer Learning—0
On the workflow, opportunities and challenges of developing foundation model in geophysics—0
Structural Transfer Learning in NL-to-Bash Semantic Parsers—0
ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks—0
On Training Sketch Recognizers for New Domains—0
AFFAKT: A Hierarchical Optimal Transport based Method for Affective Facial Knowledge Transfer in Video Deception Detection—0
On Transferability of Prompt Tuning for Natural Language Processing—0
On Transfer in Classification: How Well do Subsets of Classes Generalize?—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