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 8451–8500 of 10307 papers

TitleStatusHype
Building Robust Industrial Applicable Object Detection Models Using Transfer Learning and Single Pass Deep Learning Architectures—0
Buildings Classification using Very High Resolution Satellite Imagery—0
Bures Joint Distribution Alignment with Dynamic Margin for Unsupervised Domain Adaptation—0
Burgers' pinns with implicit euler transfer learning—0
Bypassing Optimization Complexity through Transfer Learning & Deep Neural Nets for Speech Intelligibility Improvement—0
C2KD: Bridging the Modality Gap for Cross-Modal Knowledge Distillation—0
CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learning—0
CactusNets: Layer Applicability as a Metric for Transfer Learning—0
CAE-DFKD: Bridging the Transferability Gap in Data-Free Knowledge Distillation—0
CAKD: A Correlation-Aware Knowledge Distillation Framework Based on Decoupling Kullback-Leibler Divergence—0
Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration—0
Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift—0
CALLIC: Content Adaptive Learning for Lossless Image Compression—0
Calliffusion: Chinese Calligraphy Generation and Style Transfer with Diffusion Modeling—0
CamemBERT-bio: Leveraging Continual Pre-training for Cost-Effective Models on French Biomedical Data—0
Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning—0
Camouflaged Variational Graph AutoEncoder against Attribute Inference Attacks for Cross-Domain Recommendation—0
Can Attention-based Transformers Explain or Interpret Cyberbullying Detection?—0
Can bidirectional encoder become the ultimate winner for downstream applications of foundation models?—0
Improving Cancer Hallmark Classification with BERT-based Deep Learning Approach—0
Cancer Registry Information Extraction via Transfer Learning—0
Can LLMs Grade Short-Answer Reading Comprehension Questions : An Empirical Study with a Novel Dataset—0
Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?—0
Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies?—0
Can Multisensory Cues in VR Help Train Pattern Recognition to Citizen Scientists?—0
Canoe : A System for Collaborative Learning for Neural Nets—0
Can Semantic Labels Assist Self-Supervised Visual Representation Learning?—0
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?—0
Cantonese Automatic Speech Recognition Using Transfer Learning from Mandarin—0
Can We Trust LLMs? Mitigate Overconfidence Bias in LLMs through Knowledge Transfer—0
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions—0
Developing a Novel Holistic, Personalized Dementia Risk Prediction Model via Integration of Machine Learning and Network Systems Biology Approaches—0
Capsule networks for low-data transfer learning—0
Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer—0
CarbNN: A Novel Active Transfer Learning Neural Network To Build De Novo Metal Organic Frameworks (MOFs) for Carbon Capture—0
Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts—0
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era—0
Case Study of Model Adaptation: Transfer Learning and Online Learning—0
Cashew dataset generation using augmentation and RaLSGAN and a transfer learning based tinyML approach towards disease detection—0
Cataloging Accreted Stars within Gaia DR2 using Deep Learning—0
CAT: Caution Aware Transfer in Reinforcement Learning via Distributional Risk—0
Adversarial Transfer Learning for Cross-domain Visual Recognition—0
CATrans: Context and Affinity Transformer for Few-Shot Segmentation—0
Causal Categorization of Mental Health Posts using Transformers—0
Causal Discovery Inspired Unsupervised Domain Adaptation for Emotion-Cause Pair Extraction—0
Causal Inference based Transfer Learning with LLMs: An Efficient Framework for Industrial RUL Prediction—0
Causal Inference from Small High-dimensional Datasets—0
Causal-Invariant Cross-Domain Out-of-Distribution Recommendation—0
Causality in Neural Networks -- An Extended Abstract—0
Transfer Learning for Individual Treatment Effect Estimation—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