Formality Transfer

Formality transfer, in its broadest sense, aims to leverage knowledge learned in one context (the "source") to improve performance in a related but different context (the "target"). Current research focuses on adapting this concept across diverse domains, employing techniques like transfer learning within neural networks (including transformers and convolutional neural networks), multi-armed bandit algorithms, and knowledge distillation. This research is significant because it addresses the challenge of data scarcity in many applications, improving efficiency and performance in areas such as financial prediction, robotic manipulation, and natural language processing.

Papers

November 20, 2022

A Comparative Analysis of Transfer Learning-based Techniques for the Classification of Melanocytic Nevi
Sanya Sinha, Nilay Gupta
Convolutional Neural Network Classification Code Comparative Study Deep Convolutional Neural Network Formality Transfer Skin Cancer Learning Based Method Lesion Localization

November 17, 2022

November 10, 2022

Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication
Mohamad Wazzeh, Hakima Ould-Slimane, Chamseddine Talhi, Azzam Mourad, Mohsen Guizani
Machine Learning Federated Learning Formality Transfer Identity Verification Federated Learning Approach Behavioral Biometrics Warmup Period

November 4, 2022

Rethinking the transfer learning for FCN based polyp segmentation in colonoscopy
Yan Wen, Lei Zhang, Xiangli Meng, Xujiong Ye
Formality Transfer Polyp Segmentation Segmentation Pipeline Polyp Segmentation Datasets

October 28, 2022

Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer
Andrius Ovsianas, Jason Ramapuram, Dan Busbridge, Eeshan Gunesh Dhekane, Russ Webb
Native Robustness Self Supervised Formality Transfer Self Supervised Representation Learning Fine Tuned Model Self Supervised Learning Method Weight Consolidation Bayesian Continual Learning

October 25, 2022

DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality
Ankur Handa, Arthur Allshire, Viktor Makoviychuk, Aleksei Petrenko, Ritvik Singh, Jingzhou Liu, Denys Makoviichuk, Karl Van Wyk, Alexander Zhurkevich, Balakumar Sundaralingam, Yashraj Narang, Jean-Francois Lafleche, Dieter Fox, Gavriel State
Deep Reinforcement Learning Real World Simulation Study Formality Transfer Dexterous Manipulation Hand Manipulation Dexterous Robotic Manipulation Anthropomorphic Robotic Hand

October 18, 2022

Transfer learning with affine model transformation
Shunya Minami, Kenji Fukumizu, Yoshihiro Hayashi, Ryo Yoshida
Pre Trained Model Domain Shift Formality Transfer Affine Transformation Affine Model

October 8, 2022

October 5, 2022

September 25, 2022

Transfer learning for self-supervised, blind-spot seismic denoising
Claire Birnie, Tariq Alkhalifah
Denoising Process Formality Transfer Noisy Data Seismic Data Denoising Capability

September 15, 2022

One-Shot Transfer of Affordance Regions? AffCorrs!
Denis Hadjivelichkov, Sicelukwanda Zwane, Marc Peter Deisenroth, Lourdes Agapito, Dimitrios Kanoulas
Affordance Learning Formality Transfer One Shot Image Descriptor Shot Part Segmentation

August 23, 2022

Transfer Learning-based State of Health Estimation for Lithium-ion Battery with Cycle Synchronization
Kate Qi Zhou, Yan Qin, Chau Yuen
Transfer Learning Formality Transfer State of Health Lithium Ion State of Health Estimation

August 17, 2022

Auto-segmentation of Hip Joints using MultiPlanar UNet with Transfer learning
Peidi Xu, Faezeh Moshfeghifar, Torkan Gholamalizadeh, Michael Bachmann Nielsen, Kenny Erleben, Sune Darkner
Segmentation Model Formality Transfer Accurate Segmentation Automatic Segmentation UNet Based Knee Joint Joint Segmentation Poor Segmentation

August 13, 2022

Confidence Matters: Inspecting Backdoors in Deep Neural Networks via Distribution Transfer
Tong Wang, Yuan Yao, Feng Xu, Miao Xu, Shengwei An, Ting Wang
Deep Neural Network Backdoor Attack Formality Transfer Backdoor Trigger Confidence Aware Effective Backdoor Attack

August 9, 2022

How Well Do Vision Transformers (VTs) Transfer To The Non-Natural Image Domain? An Empirical Study Involving Art Classification
Vincent Tonkes, Matthia Sabatelli
Convolutional Neural Network Vision Transformer Transfer Learning Formality Transfer Image Domain

August 7, 2022

Formality Transfer

Papers

A Comparative Analysis of Transfer Learning-based Techniques for the Classification of Melanocytic Nevi

Brain informed transfer learning for categorizing construction hazards

Transfer learning for tensor Gaussian graphical models

Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication

Rethinking the transfer learning for FCN based polyp segmentation in colonoscopy

Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer

DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality

Transfer learning with affine model transformation

Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation

Data-Efficiency with a Single GPU: An Exploration of Transfer Methods for Small Language Models

On Neural Consolidation for Transfer in Reinforcement Learning

Development and validation of deep learning based embryo selection across multiple days of transfer

Transfer learning for self-supervised, blind-spot seismic denoising

One-Shot Transfer of Affordance Regions? AffCorrs!

Transfer Learning-based State of Health Estimation for Lithium-ion Battery with Cycle Synchronization

Auto-segmentation of Hip Joints using MultiPlanar UNet with Transfer learning

Confidence Matters: Inspecting Backdoors in Deep Neural Networks via Distribution Transfer

How Well Do Vision Transformers (VTs) Transfer To The Non-Natural Image Domain? An Empirical Study Involving Art Classification

On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks

Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer