Lifelong Twin Generative Adversarial Networks

By Fei Ye, Adrian G. Bors. 2021

14 citations recorded by the source corpus.

In this paper, we propose a new continuously learning generative model, called the Lifelong Twin Generative Adversarial Networks (LT-GANs). LT-GANs learns a sequence of tasks from several databases and its architecture consists of three components: two identical generators, namely the Teacher and Assistant, and one Discriminator. In order to allow for the LT-GANs to learn new concepts without forgetting, we introduce a new lifelong training approach, namely Lifelong Adversarial Knowledge Distillation (LAKD), which encourages the Teacher and Assistant to alternately teach each other, while learning a new database. This training approach favours transferring knowledge from a more knowledgeable player to another player which knows less information about a previously given task.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

1. Fei Ye
2. Adrian G. Bors

Citations in the corpus

• Task-Free Continual Learning via Online Discrepancy Distance Learning (2022, neutral): “Another kind of approaches for continual learning focuses on the memory system, which usually employs a small memory buffer [3, 5, 6, 28, 36, 44, 59] for storing some past data or trains a generator to provide the replay samples when learning new tasks [38, 43, 46, 47, 58, 57, 52].”
• Lifelong Generative Modelling Using Dynamic Expansion Graph Model (2021, methodology): “2020), using Generative Adversarial Nets (GANs) or VAEs (Achille et al. 2018; Ramapuram, Gregorova, and Kalousis 2020; Ye and Bors 2021g; Shin et al. 2017; Ye and Bors 2020a,b, 2021a) aiming to reproduce previously learned data samples in order to attempt to overcome forgetting.”
• Lifelong Infinite Mixture Model Based on Knowledge-Driven Dirichlet Process (2021, background): “Memory replay approaches would either use a generator [1, 34, 33, 41, 56, 51, 52, 55, 57] or a memory buffer [2, 4, 46] as a replay mechanism generating data, which is statistically consistent to the previously learnt knowledge.”
• Continual Variational Autoencoder Learning via Online Cooperative Memorization (2022, background): “Other approaches would employ a small buffer to store a few past data [3,10,53] or would train a generator as a generative replay network that provides pseudo data samples for the future task learning [2,44,45,49,57,58,59,66,69].”
• Learning an Evolved Mixture Model for Task-Free Continual Learning (2022, background): “However, deep learning systems would usually perform well on individual tasks [2, 3] but suffer from dramatic performance loss when training on several different tasks sequentially [4, 5, 6].”