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 6351–6400 of 10307 papers

TitleStatusHype
Hybrid Classical-Quantum method for Diabetic Foot Ulcer Classification—0
Text-based automatic personality prediction: A bibliographic review—0
TinyFedTL: Federated Transfer Learning on Tiny Devices—0
Attention module improves both performance and interpretability of 4D fMRI decoding neural network—0
GROWN: GRow Only When Necessary for Continual Learning—0
Transfer Learning Approaches for Knowledge Discovery in Grid-based Geo-Spatiotemporal Data—0
Universal Recurrent Neural Network Grammar—0
Aspect-Based Sentiment Analysis and Singer Name Entity Recognition using Parameter Generation Network Based Transfer Learning—0
A Preliminary Study on Environmental Sound Classification Leveraging Large-Scale Pretrained Model and Semi-Supervised Learning—0
CCS-GAN: COVID-19 CT-scan classification with very few positive training images—0
Exploiting Low-Resource Code-Switching Data to Mandarin-English Speech Recognition Systems—0
Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning—0
Factored couplings in multi-marginal optimal transport via difference of convex programming—0
A Study on Using Transfer Learning to Improve BERT Model for Emotional Classification of Chinese Lyrics—0
Biologically Plausible Training Mechanisms for Self-Supervised Learning in Deep NetworksCode0
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors—0
Selective transfer learning with adversarial training for stock movement prediction—0
Multi-granular Legal Topic Classification on Greek LegislationCode0
Transferability Estimation for Semantic Segmentation Task—0
Topological Vanilla Transfer Learning—0
Transfer Learning for Bayesian HPO with End-to-End Meta-Features—0
Transliteration: A Simple Technique For Improving Multilingual Language Modeling—0
Unsupervised Domain Adaptation By Optimal Transportation Of Clusters Between Domains—0
Representation Topology Divergence: A Method for Comparing Neural Network Representations.—0
Text-Driven Image Manipulation via Semantic-Aware Knowledge Transfer—0
A Scaling Law for Syn-to-Real Transfer: How Much Is Your Pre-training Effective?—0
MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation—0
A theoretically grounded characterization of feature representations—0
Partially Relaxed Masks for Lightweight Knowledge Transfer without Forgetting in Continual Learning—0
Fundamental Limits of Transfer Learning in Binary Classifications—0
The Details Matter: Preventing Class Collapse in Supervised Contrastive Learning—0
Early-Stopping for Meta-Learning: Estimating Generalization from the Activation Dynamics—0
Selective Token Generation for Few-shot Language Modeling—0
Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation—0
Auto-Transfer: Learning to Route Transferable Representations—0
Dictionary Learning Under Generative Coefficient Priors with Applications to Compression—0
ConVAEr: Convolutional Variational AutoEncodeRs for incremental similarity learning—0
Coherent and Consistent Relational Transfer Learning with Autoencoders—0
Provably Robust Transfer—0
How to Adapt Your Large-Scale Vision-and-Language Model—0
Head2Toe: Utilizing Intermediate Representations for Better OOD Generalization—0
A Semi-supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life PredictionCode0
Measuring the Effectiveness of Self-Supervised Learning using Calibrated Learning Curves—0
Don’t throw away that linear head: Few-shot protein fitness prediction with generative models—0
A Permutation-Invariant Representation of Neural Networks with Neuron Embeddings—0
MOBA: Multi-teacher Model Based Reinforcement Learning—0
Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning—0
Generalisation in Lifelong Reinforcement Learning through Logical Composition—0
Overcoming Label Ambiguity with Multi-label Iterated Learning—0
Multi-batch Reinforcement Learning via Sample Transfer and Imitation Learning—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