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 6201–6225 of 10307 papers

TitleStatusHype
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps—0
Learning to Generalize Compositionally by Transferring Across Semantic Parsing TasksCode0
Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategy—0
Speaker Generation—0
Open-Set Crowdsourcing using Multiple-Source Transfer Learning—0
Convolutional Gated MLP: Combining Convolutions & gMLP—0
Action Recognition using Transfer Learning and Majority Voting for CSGO—0
Data Selection for Efficient Model Update in Federated Learning—0
Disengagement Cause-and-Effect Relationships Extraction Using an NLP Pipeline—0
Sexism Identification in Tweets and Gabs using Deep Neural Networks—0
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms—0
Successor Feature Neural Episodic Control—0
Skin Cancer Classification using Inception Network and Transfer Learning—0
On the Application of Data-Driven Deep Neural Networks in Linear and Nonlinear Structural Dynamics—0
Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model—0
Deep Learning Algorithms for Hedging with FrictionsCode0
Sentence encoding for Dialogue Act classification—0
Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding TasksCode0
Predicting the Location of Bicycle-sharing Stations using OpenStreetMap DataCode0
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm—0
Come hither or go away? Recognising pre-electoral coalition signals in the news—0
Japanese Zero Anaphora Resolution Can Benefit from Parallel Texts Through Neural Transfer Learning—0
Meta Distant Transfer Learning for Pre-trained Language Models—0
jurBERT: A Romanian BERT Model for Legal Judgement Prediction—0
Crosslingual Transfer Learning for Relation and Event Extraction via Word Category and Class Alignments—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