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 7301–7325 of 10307 papers

TitleStatusHype
The Frechet Distance of training and test distribution predicts the generalization gap—0
Bayesian Active Learning in the Presence of Nuisance Parameters—0
The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning—0
The Global Banking Standards QA Dataset (GBS-QA)—0
The HIT-SCIR System for End-to-End Parsing of Universal Dependencies—0
The impact of data set similarity and diversity on transfer learning success in time series forecasting—0
The Impact of Geometric Complexity on Neural Collapse in Transfer Learning—0
An Evaluation of Transfer Learning for Classifying Sales Engagement Emails at Large Scale—0
The impact of near domain transfer on biomedical named entity recognition—0
Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning—0
The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments—0
The Impact of Selectional Preference Agreement on Semantic Relational Similarity—0
The Importance of the Instantaneous Phase for classification using Convolutional Neural Networks—0
The Importance of the Instantaneous Phase in Detecting Faces with Convolutional Neural Networks—0
The Information Complexity of Learning Tasks, their Structure and their Distance—0
Can Attention-based Transformers Explain or Interpret Cyberbullying Detection?—0
The iWildCam 2019 Challenge Dataset—0
The JHU/KyotoU Speech Translation System for IWSLT 2018—0
The Joy of Neural Painting—0
The Less the Merrier? Investigating Language Representation in Multilingual Models—0
The LMU Munich System for the WMT20 Very Low Resource Supervised MT Task—0
Can bidirectional encoder become the ultimate winner for downstream applications of foundation models?—0
The Master Key Filters Hypothesis: Deep Filters Are General—0
Improving Cancer Hallmark Classification with BERT-based Deep Learning Approach—0
The Missing Link: Finding label relations across datasets—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