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 8351–8400 of 10307 papers

TitleStatusHype
Discriminative Joint Probability Maximum Mean Discrepancy (DJP-MMD) for Domain AdaptationCode0
Transfer Learning via Minimizing the Performance Gap Between DomainsCode0
End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware—0
Induction of Subgoal Automata for Reinforcement Learning—0
Improving Voice Separation by Incorporating End-to-end Speech RecognitionCode0
E-Stitchup: Data Augmentation for Pre-Trained Embeddings—0
AdapNet: Adaptability Decomposing Encoder-Decoder Network for Weakly Supervised Action Recognition and Localization—0
RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learningCode0
Brain age prediction using deep learning uncovers associated sequence variantsCode0
Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection—0
Transfer Learning in Visual and Relational Reasoning—0
A Unified Deep Learning Approach for Prediction of Parkinson's Disease—0
Disentangled Cumulants Help Successor Representations Transfer to New Tasks—0
Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning—0
Facial Landmark Correlation Analysis—0
Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer LearningCode0
A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features—0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator—0
Continual Learning with Adaptive Weights (CLAW)—0
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning—0
A Conceptual Framework for Lifelong Learning—0
Cantonese Automatic Speech Recognition Using Transfer Learning from Mandarin—0
Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery—0
Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning—0
Inspect Transfer Learning Architecture with Dilated Convolution—0
Transfer Learning Toolkit: Primers and BenchmarksCode0
Eliminating artefacts in Polarimetric Images using Deep LearningCode0
Commit2Vec: Learning Distributed Representations of Code Changes—0
Efficient Hardware Implementation of Incremental Learning and Inference on Chip—0
Unsupervised Representation Learning by Discovering Reliable Image Relations—0
Towards Making Deep Transfer Learning Never Hurt—0
Walking the Tightrope: An Investigation of the Convolutional Autoencoder BottleneckCode0
Transfer Learning of fMRI Dynamics—0
Liver Steatosis Segmentation with Deep Learning Methods—0
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data—0
QC-Automator: Deep Learning-based Automated Quality Control for Diffusion MR Images—0
Deep Discriminative Fine-Tuning for Cancer Type Classification—0
Deep Learning for Over-the-Air Non-Orthogonal Signal Classification—0
A Smartphone-Based Skin Disease Classification Using MobileNet CNN—0
BiNet: Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks—0
AMPL: A Data-Driven Modeling Pipeline for Drug DiscoveryCode0
Random Projections of Mel-Spectrograms as Low-Level Features for Automatic Music Genre Classification—0
NegBERT: A Transfer Learning Approach for Negation Detection and Scope ResolutionCode0
Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning—0
Transfer Value Iteration Networks—0
On Architectures for Including Visual Information in Neural Language Models for Image DescriptionCode0
Missing Features Reconstruction and Its Impact on Classification Accuracy—0
How Language-Neutral is Multilingual BERT?Code0
Extracting temporal features into a spatial domain using autoencoders for sperm video analysisCode0
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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