26 research outputs found
A Novel Progressive Multi-label Classifier for Classincremental Data
In this paper, a progressive learning algorithm for multi-label
classification to learn new labels while retaining the knowledge of previous
labels is designed. New output neurons corresponding to new labels are added
and the neural network connections and parameters are automatically
restructured as if the label has been introduced from the beginning. This work
is the first of the kind in multi-label classifier for class-incremental
learning. It is useful for real-world applications such as robotics where
streaming data are available and the number of labels is often unknown. Based
on the Extreme Learning Machine framework, a novel universal classifier with
plug and play capabilities for progressive multi-label classification is
developed. Experimental results on various benchmark synthetic and real
datasets validate the efficiency and effectiveness of our proposed algorithm.Comment: 5 pages, 3 figures, 4 table
CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation
Class incremental semantic segmentation aims to strike a balance between the model’s stability and plasticity by maintaining old knowledge while adapting to new concepts. However, most state-of-the-art methods use the freeze strategy for stability, which compromises the model’s plasticity. In contrast, releasing parameter training for plasticity could lead to the best performance for all categories, but this requires discriminative feature representation. Therefore, we prioritize the model’s plasticity and propose the Contrast inter- and intra-class representations for Incremental Segmentation (CoinSeg), which pursues discriminative representations for flexible parameter tuning. Inspired by the Gaussian mixture model that samples from a mixture of Gaussian distributions, CoinSeg emphasizes intra-class diversity with multiple contrastive representation centroids. Specifically, we use mask proposals to identify regions with strong objectness that are likely to be diverse instances/centroids of a category. These mask proposals are then used for contrastive representations to reinforce intra-class diversity. Meanwhile, to avoid bias from intra-class diversity, we also apply category-level pseudo-labels to enhance category-level consistency and inter-category diversity. Additionally, CoinSeg ensures the model’s stability and alleviates forgetting through a specific flexible tuning strategy. We validate CoinSeg on Pascal VOC 2012 and ADE20K datasets with multiple incremental scenarios and achieve superior results compared to previous state-of-the-art methods, especially in more challenging and realistic long-term scenarios. Code is available at https://github.com/zkzhang98/CoinSeg
Recent Advances of Continual Learning in Computer Vision: An Overview
In contrast to batch learning where all training data is available at once,
continual learning represents a family of methods that accumulate knowledge and
learn continuously with data available in sequential order. Similar to the
human learning process with the ability of learning, fusing, and accumulating
new knowledge coming at different time steps, continual learning is considered
to have high practical significance. Hence, continual learning has been studied
in various artificial intelligence tasks. In this paper, we present a
comprehensive review of the recent progress of continual learning in computer
vision. In particular, the works are grouped by their representative
techniques, including regularization, knowledge distillation, memory,
generative replay, parameter isolation, and a combination of the above
techniques. For each category of these techniques, both its characteristics and
applications in computer vision are presented. At the end of this overview,
several subareas, where continuous knowledge accumulation is potentially
helpful while continual learning has not been well studied, are discussed
Fine-Grained Knowledge Selection and Restoration for Non-Exemplar Class Incremental Learning
Non-exemplar class incremental learning aims to learn both the new and old
tasks without accessing any training data from the past. This strict
restriction enlarges the difficulty of alleviating catastrophic forgetting
since all techniques can only be applied to current task data. Considering this
challenge, we propose a novel framework of fine-grained knowledge selection and
restoration. The conventional knowledge distillation-based methods place too
strict constraints on the network parameters and features to prevent
forgetting, which limits the training of new tasks. To loose this constraint,
we proposed a novel fine-grained selective patch-level distillation to
adaptively balance plasticity and stability. Some task-agnostic patches can be
used to preserve the decision boundary of the old task. While some patches
containing the important foreground are favorable for learning the new task.
Moreover, we employ a task-agnostic mechanism to generate more realistic
prototypes of old tasks with the current task sample for reducing classifier
bias for fine-grained knowledge restoration. Extensive experiments on CIFAR100,
TinyImageNet and ImageNet-Subset demonstrate the effectiveness of our method.
Code is available at https://github.com/scok30/vit-cil.Comment: to appear at AAAI 202
CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation
Class incremental semantic segmentation aims to strike a balance between the
model's stability and plasticity by maintaining old knowledge while adapting to
new concepts. However, most state-of-the-art methods use the freeze strategy
for stability, which compromises the model's plasticity.In contrast, releasing
parameter training for plasticity could lead to the best performance for all
categories, but this requires discriminative feature representation.Therefore,
we prioritize the model's plasticity and propose the Contrast inter- and
intra-class representations for Incremental Segmentation (CoinSeg), which
pursues discriminative representations for flexible parameter tuning. Inspired
by the Gaussian mixture model that samples from a mixture of Gaussian
distributions, CoinSeg emphasizes intra-class diversity with multiple
contrastive representation centroids. Specifically, we use mask proposals to
identify regions with strong objectness that are likely to be diverse
instances/centroids of a category. These mask proposals are then used for
contrastive representations to reinforce intra-class diversity. Meanwhile, to
avoid bias from intra-class diversity, we also apply category-level
pseudo-labels to enhance category-level consistency and inter-category
diversity. Additionally, CoinSeg ensures the model's stability and alleviates
forgetting through a specific flexible tuning strategy. We validate CoinSeg on
Pascal VOC 2012 and ADE20K datasets with multiple incremental scenarios and
achieve superior results compared to previous state-of-the-art methods,
especially in more challenging and realistic long-term scenarios. Code is
available at https://github.com/zkzhang98/CoinSeg.Comment: Accepted by ICCV 202
Wakening Past Concepts without Past Data: Class-Incremental Learning from Online Placebos
Not forgetting old class knowledge is a key challenge for class-incremental
learning (CIL) when the model continuously adapts to new classes. A common
technique to address this is knowledge distillation (KD), which penalizes
prediction inconsistencies between old and new models. Such prediction is made
with almost new class data, as old class data is extremely scarce due to the
strict memory limitation in CIL. In this paper, we take a deep dive into KD
losses and find that "using new class data for KD" not only hinders the model
adaption (for learning new classes) but also results in low efficiency for
preserving old class knowledge. We address this by "using the placebos of old
classes for KD", where the placebos are chosen from a free image stream, such
as Google Images, in an automatical and economical fashion. To this end, we
train an online placebo selection policy to quickly evaluate the quality of
streaming images (good or bad placebos) and use only good ones for one-time
feed-forward computation of KD. We formulate the policy training process as an
online Markov Decision Process (MDP), and introduce an online learning
algorithm to solve this MDP problem without causing much computation costs. In
experiments, we show that our method 1) is surprisingly effective even when
there is no class overlap between placebos and original old class data, 2) does
not require any additional supervision or memory budget, and 3) significantly
outperforms a number of top-performing CIL methods, in particular when using
lower memory budgets for old class exemplars, e.g., five exemplars per class.Comment: Accepted to WACV 2024. Code:
https://github.com/yaoyao-liu/online-placebo
Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects
Medical imaging analysis has witnessed remarkable advancements even
surpassing human-level performance in recent years, driven by the rapid
development of advanced deep-learning algorithms. However, when the inference
dataset slightly differs from what the model has seen during one-time training,
the model performance is greatly compromised. The situation requires restarting
the training process using both the old and the new data which is
computationally costly, does not align with the human learning process, and
imposes storage constraints and privacy concerns. Alternatively, continual
learning has emerged as a crucial approach for developing unified and
sustainable deep models to deal with new classes, tasks, and the drifting
nature of data in non-stationary environments for various application areas.
Continual learning techniques enable models to adapt and accumulate knowledge
over time, which is essential for maintaining performance on evolving datasets
and novel tasks. This systematic review paper provides a comprehensive overview
of the state-of-the-art in continual learning techniques applied to medical
imaging analysis. We present an extensive survey of existing research, covering
topics including catastrophic forgetting, data drifts, stability, and
plasticity requirements. Further, an in-depth discussion of key components of a
continual learning framework such as continual learning scenarios, techniques,
evaluation schemes, and metrics is provided. Continual learning techniques
encompass various categories, including rehearsal, regularization,
architectural, and hybrid strategies. We assess the popularity and
applicability of continual learning categories in various medical sub-fields
like radiology and histopathology..
Class-Incremental Exemplar Compression for Class-Incremental Learning
Exemplar-based class-incremental learning (CIL) finetunes the model with all
samples of new classes but few-shot exemplars of old classes in each
incremental phase, where the "few-shot" abides by the limited memory budget. In
this paper, we break this "few-shot" limit based on a simple yet surprisingly
effective idea: compressing exemplars by downsampling non-discriminative pixels
and saving "many-shot" compressed exemplars in the memory. Without needing any
manual annotation, we achieve this compression by generating 0-1 masks on
discriminative pixels from class activation maps (CAM). We propose an adaptive
mask generation model called class-incremental masking (CIM) to explicitly
resolve two difficulties of using CAM: 1) transforming the heatmaps of CAM to
0-1 masks with an arbitrary threshold leads to a trade-off between the coverage
on discriminative pixels and the quantity of exemplars, as the total memory is
fixed; and 2) optimal thresholds vary for different object classes, which is
particularly obvious in the dynamic environment of CIL. We optimize the CIM
model alternatively with the conventional CIL model through a bilevel
optimization problem. We conduct extensive experiments on high-resolution CIL
benchmarks including Food-101, ImageNet-100, and ImageNet-1000, and show that
using the compressed exemplars by CIM can achieve a new state-of-the-art CIL
accuracy, e.g., 4.8 percentage points higher than FOSTER on 10-Phase
ImageNet-1000. Our code is available at https://github.com/xfflzl/CIM-CIL.Comment: Accepted to CVPR 202