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 4801–4850 of 10307 papers

TitleStatusHype
Target Aware Network Architecture Search and Compression for Efficient Knowledge TransferCode0
AutoKE: An automatic knowledge embedding framework for scientific machine learningCode1
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning—0
Automatic Tuberculosis and COVID-19 cough classification using deep learning—0
CoDo: Contrastive Learning with Downstream Background Invariance for Detection—0
Object Detection in Indian Food Platters using Transfer Learning with YOLOv4—0
Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulenceCode0
Sub-Word Alignment Is Still Useful: A Vest-Pocket Method for Enhancing Low-Resource Machine TranslationCode0
An Effective Scheme for Maize Disease Recognition based on Deep Networks—0
ProQA: Structural Prompt-based Pre-training for Unified Question AnsweringCode1
Transfer Learning Based Efficient Traffic Prediction with Limited Training Data—0
On the Use of BERT for Automated Essay Scoring: Joint Learning of Multi-Scale Essay RepresentationCode1
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks—0
Training from Zero: Radio Frequency Machine Learning Data Quantity Forecasting—0
Keratoconus Classifier for Smartphone-based Corneal Topographer—0
Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attentionCode1
Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding—0
Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning—0
Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance—0
RCMNet: A deep learning model assists CAR-T therapy for leukemia—0
Dynamically writing coupled memories using a reinforcement learning agent, meeting physical bounds—0
Large Scale Transfer Learning for Differentially Private Image Classification—0
Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning—0
Understanding Transfer Learning for Chest Radiograph Clinical Report Generation with Modified Transformer Architectures—0
ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks—0
Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models—0
XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression ExtractionCode0
MTTrans: Cross-Domain Object Detection with Mean-Teacher TransformerCode1
Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity?—0
Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer LearningCode0
FINETUNA: Fine-tuning Accelerated Molecular Simulations—0
Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 LanguagesCode1
Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive LearningCode1
Evaluating zero-shot transfers and multilingual models for dependency parsing and POS tagging within the low-resource language family Tupían—0
S^4-Tuning: A Simple Cross-lingual Sub-network Tuning Method—0
A Checkpoint on Multilingual Misogyny Identification—0
Towards Detecting Political Bias in Hindi News Articles—0
Transfer Learning and Prediction Consistency for Detecting Offensive Spans of Text—0
Uncertainty Regularized Multi-Task Learning—0
What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural textCode0
When does CLIP generalize better than unimodal models? When judging human-centric concepts—0
Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues—0
Leveraging Seq2seq Language Generation for Multi-level Product Issue Identification—0
Cross-lingual Semantic Role Labelling with the ValPaL Database Knowledge—0
Challenges in including extra-linguistic context in pre-trained language models—0
Shallow Parsing for Nepal Bhasa Complement Clauses—0
MUCS@Text-LT-EDI@ACL 2022: Detecting Sign of Depression from Social Media Text using Supervised Learning Approach—0
On Target Representation in Continuous-output Neural Machine Translation—0
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition—0
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing?—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