130,160 research outputs found
Improved Federated Learning for Handling Long-tail Words
Automatic speech recognition (ASR) machine learning models are deployed on client devices that include speech interfaces. ASR models can benefit from continuous learning and adaptation to large-scale changes, e.g., as new words are added to the vocabulary. While federated learning can be utilized to enable continuous learning for ASR models in a privacy preserving manner, the trained model can perform poorly on rarely occurring, long-tail words if the distribution of data used to train the model is skewed and does not adequately represent long-tail words. This disclosure describes federated learning techniques to improve ASR model quality when interpreting long-tail words given an imbalanced data distribution. Two different approaches - probabilistic sampling and client loss weighting - are described herein. In probabilistic sampling, the federated clients that include fewer long-tail words are less likely to be selected during training. In client loss weighting, incorrect predictions on long-tail words are more heavily penalized than for other words
Constructing Balance from Imbalance for Long-tailed Image Recognition
Long-tailed image recognition presents massive challenges to deep learning
systems since the imbalance between majority (head) classes and minority (tail)
classes severely skews the data-driven deep neural networks. Previous methods
tackle with data imbalance from the viewpoints of data distribution, feature
space, and model design, etc.In this work, instead of directly learning a
recognition model, we suggest confronting the bottleneck of head-to-tail bias
before classifier learning, from the previously omitted perspective of
balancing label space. To alleviate the head-to-tail bias, we propose a concise
paradigm by progressively adjusting label space and dividing the head classes
and tail classes, dynamically constructing balance from imbalance to facilitate
the classification. With flexible data filtering and label space mapping, we
can easily embed our approach to most classification models, especially the
decoupled training methods. Besides, we find the separability of head-tail
classes varies among different features with different inductive biases. Hence,
our proposed model also provides a feature evaluation method and paves the way
for long-tailed feature learning. Extensive experiments show that our method
can boost the performance of state-of-the-arts of different types on
widely-used benchmarks. Code is available at https://github.com/silicx/DLSA.Comment: Accepted to ECCV 202
Robust Feature Learning and Global Variance-Driven Classifier Alignment for Long-Tail Class Incremental Learning
This paper introduces a two-stage framework designed to enhance long-tail
class incremental learning, enabling the model to progressively learn new
classes, while mitigating catastrophic forgetting in the context of long-tailed
data distributions. Addressing the challenge posed by the under-representation
of tail classes in long-tail class incremental learning, our approach achieves
classifier alignment by leveraging global variance as an informative measure
and class prototypes in the second stage. This process effectively captures
class properties and eliminates the need for data balancing or additional layer
tuning. Alongside traditional class incremental learning losses in the first
stage, the proposed approach incorporates mixup classes to learn robust feature
representations, ensuring smoother boundaries. The proposed framework can
seamlessly integrate as a module with any class incremental learning method to
effectively handle long-tail class incremental learning scenarios. Extensive
experimentation on the CIFAR-100 and ImageNet-Subset datasets validates the
approach's efficacy, showcasing its superiority over state-of-the-art
techniques across various long-tail CIL settings.Comment: Accepted in WACV 202
U.S. stock market interaction network as learned by the Boltzmann Machine
We study historical dynamics of joint equilibrium distribution of stock
returns in the U.S. stock market using the Boltzmann distribution model being
parametrized by external fields and pairwise couplings. Within Boltzmann
learning framework for statistical inference, we analyze historical behavior of
the parameters inferred using exact and approximate learning algorithms. Since
the model and inference methods require use of binary variables, effect of this
mapping of continuous returns to the discrete domain is studied. The presented
analysis shows that binarization preserves market correlation structure.
Properties of distributions of external fields and couplings as well as
industry sector clustering structure are studied for different historical dates
and moving window sizes. We found that a heavy positive tail in the
distribution of couplings is responsible for the sparse market clustering
structure. We also show that discrepancies between the model parameters might
be used as a precursor of financial instabilities.Comment: 15 pages, 17 figures, 1 tabl
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature Augmentation
Personalized Federated Learning (PFL) aims to learn personalized models for
each client based on the knowledge across all clients in a privacy-preserving
manner. Existing PFL methods generally assume that the underlying global data
across all clients are uniformly distributed without considering the long-tail
distribution. The joint problem of data heterogeneity and long-tail
distribution in the FL environment is more challenging and severely affects the
performance of personalized models. In this paper, we propose a PFL method
called Federated Learning with Adversarial Feature Augmentation (FedAFA) to
address this joint problem in PFL. FedAFA optimizes the personalized model for
each client by producing a balanced feature set to enhance the local minority
classes. The local minority class features are generated by transferring the
knowledge from the local majority class features extracted by the global model
in an adversarial example learning manner. The experimental results on
benchmarks under different settings of data heterogeneity and long-tail
distribution demonstrate that FedAFA significantly improves the personalized
performance of each client compared with the state-of-the-art PFL algorithm.
The code is available at https://github.com/pxqian/FedAFA.Comment: Accepted by ICASSP 202
- …