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 8251–8300 of 10307 papers

TitleStatusHype
Unsupervised Inflection Generation Using Neural Language Modeling—0
TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLPCode0
Implicit Priors for Knowledge Sharing in Bayesian Neural Networks—0
Deep Neural Network Fingerprinting by Conferrable Adversarial ExamplesCode0
Is Discriminator a Good Feature Extractor?—0
Applying Knowledge Transfer for Water Body Segmentation in Peru—0
Cross-Domain Recommendation via Preference Propagation GraphNet—0
Syntactically Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion Extraction—0
Discriminative Joint Probability Maximum Mean Discrepancy (DJP-MMD) for Domain AdaptationCode0
Online Knowledge Distillation with Diverse PeersCode0
Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain ActivityCode0
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks—0
Comparing Unsupervised Word Translation Methods Step by Step—0
Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative TransferCode0
Better Transfer Learning with Inferred Successor Maps—0
Transfer Learning via Minimizing the Performance Gap Between DomainsCode0
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer LearningCode0
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
Brain age prediction using deep learning uncovers associated sequence variantsCode0
RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learningCode0
AdapNet: Adaptability Decomposing Encoder-Decoder Network for Weakly Supervised Action Recognition and Localization—0
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
Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning—0
Disentangled Cumulants Help Successor Representations Transfer to New Tasks—0
A Unified Deep Learning Approach for Prediction of Parkinson's Disease—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
Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator—0
A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features—0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
A Conceptual Framework for Lifelong Learning—0
Cantonese Automatic Speech Recognition Using Transfer Learning from Mandarin—0
Continual Learning with Adaptive Weights (CLAW)—0
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning—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
Efficient Hardware Implementation of Incremental Learning and Inference on Chip—0
Commit2Vec: Learning Distributed Representations of Code Changes—0
Walking the Tightrope: An Investigation of the Convolutional Autoencoder BottleneckCode0
Unsupervised Representation Learning by Discovering Reliable Image Relations—0
Towards Making Deep Transfer Learning Never Hurt—0
Transfer Learning of fMRI Dynamics—0
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data—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