Data-Free Knowledge Distillation (KD) allows knowledge transfer from a trained neural network (teacher) to a more compact one (student) in the absence of original training data. Existing works use a validation set to monitor the accuracy of the student over real data and report the highest performance throughout the entire process. However, validation data may not be available at distillation time either, making it infeasible to record the student snapshot that achieved the peak accuracy. Therefore, a practical data-free KD method should be robust and ideally provide monotonically increasing student accuracy during distillation. This is challenging because the student experiences knowledge degradation due to the distribution shift of the synthetic data. A straightforward approach to overcome this issue is to store and rehearse the generated samples periodically, which increases the memory footprint and creates privacy concerns. We propose to model the distribution of the previously observed synthetic samples with a generative network. In particular, we design a Variational Autoencoder (VAE) with a training objective that is customized to learn the synthetic data representations optimally. The student is rehearsed by the generative pseudo replay technique, with samples produced by the VAE. Hence knowledge degradation can be prevented without storing any samples. Experiments on image classification benchmarks show that our method optimizes the expected value of the distilled model accuracy while eliminating the large memory overhead incurred by the sample-storing methods.
This paper proposes two novel knowledge transfer techniques for class-incremental learning (CIL). First, we propose data-free generative replay (DF-GR) to mitigate catastrophic forgetting in CIL by using synthetic samples from a generative model. In the conventional generative replay, the generative model is pre-trained for old data and shared in extra memory for later incremental learning. In our proposed DF-GR, we train a generative model from scratch without using any training data, based on the pre-trained classification model from the past, so we curtail the cost of sharing pre-trained ge…
Class Incremental Fault Diagnosis under Limited Fault Data via Supervised Contrastive Knowledge Distillation
ZJU-UIUC Joint Institute at Zhejiang University（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合研究所）；ZJU-UoE Institute at Zhejiang University（浙江大学-爱丁堡大学联合研究所）；College of Biomedical Engineering and Instrument Science at Zhejiang University（浙江大学生物医学工程与仪器科学学院）；College of Computer Science and Technology in Zhejiang University（浙江大学计算机科学与技术学院）
Our work is a simple extension of the paper "Exploration by Random Network Distillation". More in detail, we show how to efficiently combine Intrinsic Rewards with Experience Replay in order to achieve more efficient and robust exploration (with respect to PPO/RND) and consequently better results in terms of agent performances and sample efficiency. We are able to do it by using a new technique named Prioritized Oversampled Experience Replay (POER), that has been built upon the definition of what is the important experience useful to replay. Finally, we evaluate our technique on the famous Ata…
Yonsei University（延世大学）；Deakin University（迪肯大学）；Samsung Electronics Co., Ltd.（三星电子株式会社）；University of Oulu（奥卢大学）
Traditional distributed deep reinforcement learning (RL) commonly relies on exchanging the experience replay memory (RM) of each agent. Since the RM contains all state observations and action policy history, it may incur huge communication overhead while violating the privacy of each agent. Alternatively, this article presents a communication-efficient and privacy-preserving distributed RL framework, coined federated reinforcement distillation (FRD). In FRD, each agent exchanges its proxy experience replay memory (ProxRM), in which policies are locally averaged with respect to proxy states clu…
From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity
School of Software, Shandong University（山东大学软件学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）
Toward Robust Semi-supervised Regression via Dual-stream Knowledge Distillation
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences（重庆绿色智能技术研究所，中国科学院）；Chongqing School, University of Chinese Academy of Sciences（中国科学院大学重庆学院）；Singapore Management University（新加坡管理大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
South China University of Technology（华南理工大学）；Northwestern Polytechnical University（西北工业大学）
Inspired by the strong correlation between the Label Smoothing Regularization(LSR) and Knowledge distillation(KD), we propose an algorithm LsrKD for training boost by extending the LSR method to the KD regime and applying a softer temperature. Then we improve the LsrKD by a Teacher Correction(TC) method, which manually sets a constant larger proportion for the right class in the uniform distribution teacher. To further improve the performance of LsrKD, we develop a self-distillation method named Memory-replay Knowledge Distillation (MrKD) that provides a knowledgeable teacher to replace the un…
IIT Kanpur（印度理工学院坎普尔分校）；Duke University（杜克大学）；Verisk Analytics（威斯科分析公司）；University of Bath（巴斯大学）
A wide variety of methods have been developed to enable lifelong learning in conventional deep neural networks. However, to succeed, these methods require a `batch' of samples to be available and visited multiple times during training. While this works well in a static setting, these methods continue to suffer in a more realistic situation where data arrives in \emph{online streaming manner}. We empirically demonstrate that the performance of current approaches degrades if the input is obtained as a stream of data with the following restrictions: $(i)$ each instance comes one at a time and can…
RPTU Kaiserslautern-Landau（莱茵兰-普法尔茨州凯泽斯劳滕-兰道应用技术大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Heidelberg University（海德堡大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
Morphological atlases are an important tool in organismal studies, and modern high-throughput Computed Tomography (CT) facilities can produce hundreds of full-body high-resolution volumetric images of organisms. However, creating an atlas from these volumes requires accurate organ segmentation. In the last decade, machine learning approaches have achieved incredible results in image segmentation tasks, but they require large amounts of annotated data for training. In this paper, we propose a self-training framework for multi-organ segmentation in tomographic images of Medaka fish. We utilize t…
Dual-LoRA and Quality-Enhanced Pseudo Replay for Multimodal Continual Food Learning
College of Computer Science and Artificial Intelligence, Fudan University, China（复旦大学计算机科学与人工智能学院）；Singapore Management University, Singapore（新加坡国立管理学院）
Enhancing Automatic Chord Recognition via Pseudo-Labeling and Knowledge Distillation
California State University, Fullerton, CA, USA（加州州立大学弗里蒙特分校）；Microsoft, Redmond, WA, USA（微软公司）；Beijing Normal-Hong Kong Baptist University, China（北京师范大学-香港 Baptist大学）
Knowledge Distillation (KD) utilizes training data as a transfer set to transfer knowledge from a complex network (Teacher) to a smaller network (Student). Several works have recently identified many scenarios where the training data may not be available due to data privacy or sensitivity concerns and have proposed solutions under this restrictive constraint for the classification task. Unlike existing works, we, for the first time, solve a much more challenging problem, i.e., "KD for object detection with zero knowledge about the training data and its statistics". Our proposed approach prepar…
University of Pisa（比萨大学）；Scuola Normale Superiore（比萨高等师范学校）
Replay strategies are Continual Learning techniques which mitigate catastrophic forgetting by keeping a buffer of patterns from previous experiences, which are interleaved with new data during training. The amount of patterns stored in the buffer is a critical parameter which largely influences the final performance and the memory footprint of the approach. This work introduces Distilled Replay, a novel replay strategy for Continual Learning which is able to mitigate forgetting by keeping a very small buffer (1 pattern per class) of highly informative samples. Distilled Replay builds the buffe…
Defense without Forgetting: Continual Adversarial Defense with Anisotropic & Isotropic Pseudo Replay
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies（广东省新型安全智能技术重点实验室）
The Hong Kong University of Science and Technology（香港科技大学）；Alibaba Group（阿里巴巴集团）；The Chinese University of Hong Kong（香港中文大学）
Lifelong learning (LL) is vital for advanced task-oriented dialogue (ToD) systems. To address the catastrophic forgetting issue of LL, generative replay methods are widely employed to consolidate past knowledge with generated pseudo samples. However, most existing generative replay methods use only a single task-specific token to control their models. This scheme is usually not strong enough to constrain the generative model due to insufficient information involved. In this paper, we propose a novel method, prompt conditioned VAE for lifelong learning (PCLL), to enhance generative replay by in…
In this paper, we describe NeurIPS 2019 Learning to Move - Walk Around challenge physics-based environment and present our solution to this competition which scored 1303.727 mean reward points and took 3rd place. Our method combines recent advances from both continuous- and discrete-action space reinforcement learning, such as Soft Actor-Critic and Recurrent Experience Replay in Distributed Reinforcement Learning. We trained our agent in two stages: to move somewhere at the first stage and to follow the target velocity field at the second stage. We also introduce novel Q-function split techniq…
Unsupervised Identification and Replay-based Detection (UIRD) for New Category Anomaly Detection in ECG Signal
Charles F. Dolan School of Business, Fairfield University（费尔菲尔德大学查尔斯·F·多兰商学院）；School of Business, East China University of Science and Technology（华东理工大学商学院）
Continual Learning with Diffusion-based Generative Replay for Industrial Streaming Data
Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（华南理工大学吴贤铭智能工程学院）；Pazhou Lab（琶洲实验室）；China Academy of Information and Communications Technology（中国信息通信研究院）
Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, China（中国科学院声学研究所语言声学与内容理解重点实验室）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学）
Incremental language learning with pseudo-data can alleviate catastrophic forgetting in neural networks. However, to obtain better performance, former methods have higher demands for pseudo-data of the previous tasks. The performance dramatically decreases when fewer pseudo-data are employed. In addition, the distribution of pseudo-data gradually deviates from the real data with the sequential learning of different tasks. The deviation will be greater with more tasks learned, which results in more serious catastrophic forgetting. To address these issues, we propose reminding incremental langua…
National University of Science and Technology（国立科技大学）
The ability to learn from incrementally arriving data is essential for any life-long learning system. However, standard deep neural networks forget the knowledge about the old tasks, a phenomenon called catastrophic forgetting, when trained on incrementally arriving data. We discuss the biases in current Generative Adversarial Networks (GAN) based approaches that learn the classifier by knowledge distillation from previously trained classifiers. These biases cause the trained classifier to perform poorly. We propose an approach to remove these biases by distilling knowledge from the classifier…
d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation
University of California, San Diego（加州大学圣地亚哥分校）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家实验室）；Shanghai Jiao Tong University（上海交通大学）
Georgia Institute of Technology（佐治亚理工学院）；Samsung Research America（三星美国研究院）
Modern computer vision applications suffer from catastrophic forgetting when incrementally learning new concepts over time. The most successful approaches to alleviate this forgetting require extensive replay of previously seen data, which is problematic when memory constraints or data legality concerns exist. In this work, we consider the high-impact problem of Data-Free Class-Incremental Learning (DFCIL), where an incremental learning agent must learn new concepts over time without storing generators or training data from past tasks. One approach for DFCIL is to replay synthetic images produ…
SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Chongqing University（重庆大学）；Peng Cheng Laboratory（鹏城实验室）；Wuhan AI Research（武汉人工智能研究院）
Baylor College of Medicine（贝勒医学院）；Rice University（莱斯大学）
A major obstacle to developing artificial intelligence applications capable of true lifelong learning is that artificial neural networks quickly or catastrophically forget previously learned tasks when trained on a new one. Numerous methods for alleviating catastrophic forgetting are currently being proposed, but differences in evaluation protocols make it difficult to directly compare their performance. To enable more meaningful comparisons, here we identified three distinct scenarios for continual learning based on whether task identity is known and, if it is not, whether it needs to be infe…
Looking through the past: better knowledge retention for generative replay in continual learning
IDEAS NCBR；Gdańsk University of Technology（格但斯克工业大学）；Warsaw University of Technology（华沙工业大学）；Jagiellonian University（雅盖隆大学）；Tooploox；Computer Vision Center（计算机视觉中心）；Universitat Autònoma Barcelona（巴塞罗那自治大学）
University of Central Florida（中佛罗里达大学）；University of Macau（澳门大学）；Nanjing University（南京大学）
In many scenarios, 1) data streams are generated in real time; 2) labeled data are expensive and only limited labels are available in the beginning; 3) real-world data is not always i.i.d. and data drift over time gradually; 4) the storage of historical streams is limited and model updating can only be achieved based on a very short lookback window. This learning setting limits the applicability and availability of many Machine Learning (ML) algorithms. We generalize the learning task under such setting as a semi-supervised drifted stream learning with short lookback problem (SDSL). SDSL impos…
KAIST（韩国科学技术院）；Stanford University（斯坦福大学）；NAVER AI Lab（NAVER人工智能实验室）；AITRICS
A Bayesian pseudocoreset is a small synthetic dataset for which the posterior over parameters approximates that of the original dataset. While promising, the scalability of Bayesian pseudocoresets is not yet validated in realistic problems such as image classification with deep neural networks. On the other hand, dataset distillation methods similarly construct a small dataset such that the optimization using the synthetic dataset converges to a solution with performance competitive with optimization using full data. Although dataset distillation has been empirically verified in large-scale se…
