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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8976–9000 of 10307 papers

TitleStatusHype
Classification of Astronomical Bodies by Efficient Layer Fine-Tuning of Deep Neural NetworksCode0
Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning MethodCode0
Classification of Breast Tumours Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting MethodsCode0
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural networkCode0
Classification of COVID-19 in CT Scans using Multi-Source Transfer LearningCode0
Classification of Quasars, Galaxies, and Stars in the Mapping of the Universe Multi-modal Deep LearningCode0
Classifying Textual Data with Pre-trained Vision Models through Transfer Learning and Data TransformationsCode0
CleftGAN: Adapting A Style-Based Generative Adversarial Network To Create Images Depicting Cleft Lip DeformityCode0
CLIP-based Synergistic Knowledge Transfer for Text-based Person RetrievalCode0
CL-NERIL: A Cross-Lingual Model for NER in Indian LanguagesCode0
Probing Predictions on OOD Images via Nearest CategoriesCode0
CLOSURE: Assessing Systematic Generalization of CLEVR ModelsCode0
Cluster-aware Pseudo-Labeling for Supervised Open Relation ExtractionCode0
ClusterFit: Improving Generalization of Visual RepresentationsCode0
C-Net: A Reliable Convolutional Neural Network for Biomedical Image ClassificationCode0
CNN-based Approach for Cervical Cancer Classification in Whole-Slide Histopathology ImagesCode0
CNN-based Methods for Object Recognition with High-Resolution Tactile SensorsCode0
Coevolutionary Framework for Generalized Multimodal Multi-objective OptimizationCode0
Cogni-Net: Cognitive Feature Learning through Deep Visual PerceptionCode0
CogTaskonomy: Cognitively Inspired Task Taxonomy Is Beneficial to Transfer Learning in NLPCode0
Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razorCode0
COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasetsCode0
Collaborative Deep Reinforcement LearningCode0
Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?Code0
Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer LearningCode0
Show:102550
← PrevPage 360 of 413Next →
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