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 2001–2050 of 10307 papers

TitleStatusHype
Characterizing and Avoiding Negative Transfer—0
The (In)Effectiveness of Intermediate Task Training For Domain Adaptation and Cross-Lingual Transfer Learning—0
Computation and Data Efficient Backdoor Attacks—0
A physics-based domain adaptation framework for modelling and forecasting building energy systems—0
Channel-wise pruning of neural networks with tapering resource constraint—0
Computer-aided Diagnosis of Malaria through Transfer Learning using the ResNet50 Backbone—0
A Petri Dish for Histopathology Image Analysis—0
Computer-Aided Osteoporosis Diagnosis Using Transfer Learning with Enhanced Features from Stacked Deep Learning Modules—0
Channel Scaling: A Scale-and-Select Approach for Transfer Learning—0
Computing with Categories in Machine Learning—0
Change your singer: a transfer learning generative adversarial framework for song to song conversion—0
A Permutation-Invariant Representation of Neural Networks with Neuron Embeddings—0
Action Recognition for American Sign Language—0
Concept Formation and Alignment in Language Models: Bridging Statistical Patterns in Latent Space to Concept Taxonomy—0
Concept Transfer Learning for Adaptive Language Understanding—0
Conceptual Expansion Neural Architecture Search (CENAS)—0
Assessing the Performance of Analog Training for Transfer Learning—0
Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior—0
CON: Continual Object Navigation via Data-Free Inter-Agent Knowledge Transfer in Unseen and Unfamiliar Places—0
Concrete Surface Crack Detection with Convolutional-based Deep Learning Models—0
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning—0
Condensed Sample-Guided Model Inversion for Knowledge Distillation—0
Adversarial Inductive Transfer Learning with input and output space adaptation—0
Conditional Bures Metric for Domain Adaptation—0
Conditional computation in neural networks: principles and research trends—0
Conditional Data Synthesis Augmentation—0
A Pathology-Based Machine Learning Method to Assist in Epithelial Dysplasia Diagnosis—0
Conditional Electrocardiogram Generation Using Hierarchical Variational Autoencoders—0
Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources—0
Conditional Neural Processes for Molecules—0
A General Regularization Framework for Domain Adaptation—0
Conditional Loss and Deep Euler Scheme for Time Series Generation—0
Aspect-Based Sentiment Analysis and Singer Name Entity Recognition using Parameter Generation Network Based Transfer Learning—0
Challenges in including extra-linguistic context in pre-trained language models—0
Confidence Aware Neural Networks for Skin Cancer Detection—0
Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface—0
Challenges for cognitive decoding using deep learning methods—0
Confidence Estimation in Unsupervised Deep Change Vector Analysis—0
Confidence-Nets: A Step Towards better Prediction Intervals for regression Neural Networks on small datasets—0
Confidence Preserving Machine for Facial Action Unit Detection—0
A PAC-Bayesian bound for Lifelong Learning—0
Cross-lingual Transfer Learning for Pre-trained Contextualized Language Models—0
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis—0
ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research—0
Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer—0
A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling—0
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning—0
Cross-Lingual Transfer Learning for Question Answering—0
Crosslingual Transfer Learning for Relation and Event Extraction via Word Category and Class Alignments—0
Cross-Linguistic Examination of Machine Translation Transfer 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