FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer

Federated Learning (FL) has been widely concerned for it enables decentralized learning while ensuring data privacy. However, most existing methods unrealistically assume that the classes encountered by local clients are fixed over time. After learning new classes, this assumption will make the model's catastrophic forgetting of old classes significantly severe. Moreover, due to the limitation of communication cost, it is challenging to use large-scale models in FL, which will affect the prediction accuracy. To address these challenges, we propose a novel framework, Federated Enhanced Transformer (FedET), which simultaneously achieves high accuracy and low communication cost. Specifically, FedET uses Enhancer, a tiny module, to absorb and communicate new knowledge, and applies pre-trained Transformers combined with different Enhancers to ensure high precision on various tasks. To address local forgetting caused by new classes of new tasks and global forgetting brought by non-i.i.d (non-independent and identically distributed) class imbalance across different local clients, we proposed an Enhancer distillation method to modify the imbalance between old and new knowledge and repair the non-i.i.d. problem. Experimental results demonstrate that FedET's average accuracy on representative benchmark datasets is 14.1% higher than the state-of-the-art method, while FedET saves 90% of the communication cost compared to the previous method.Comment: Accepted by 2023 International Joint Conference on Artificial Intelligence (IJCAI2023

INCPrompt: Task-Aware incremental Prompting for Rehearsal-Free Class-incremental Learning

This paper introduces INCPrompt, an innovative continual learning solution that effectively addresses catastrophic forgetting. INCPrompt's key innovation lies in its use of adaptive key-learner and task-aware prompts that capture task-relevant information. This unique combination encapsulates general knowledge across tasks and encodes task-specific knowledge. Our comprehensive evaluation across multiple continual learning benchmarks demonstrates INCPrompt's superiority over existing algorithms, showing its effectiveness in mitigating catastrophic forgetting while maintaining high performance. These results highlight the significant impact of task-aware incremental prompting on continual learning performance.Comment: Accepted by the 49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024

Pose Guided Human Image Synthesis with Partially Decoupled GAN

Pose Guided Human Image Synthesis (PGHIS) is a challenging task of transforming a human image from the reference pose to a target pose while preserving its style. Most existing methods encode the texture of the whole reference human image into a latent space, and then utilize a decoder to synthesize the image texture of the target pose. However, it is difficult to recover the detailed texture of the whole human image. To alleviate this problem, we propose a method by decoupling the human body into several parts (\eg, hair, face, hands, feet, \etc) and then using each of these parts to guide the synthesis of a realistic image of the person, which preserves the detailed information of the generated images. In addition, we design a multi-head attention-based module for PGHIS. Because most convolutional neural network-based methods have difficulty in modeling long-range dependency due to the convolutional operation, the long-range modeling capability of attention mechanism is more suitable than convolutional neural networks for pose transfer task, especially for sharp pose deformation. Extensive experiments on Market-1501 and DeepFashion datasets reveal that our method almost outperforms other existing state-of-the-art methods in terms of both qualitative and quantitative metrics.Comment: 16 pages, 14th Asian Conference on Machine Learning conferenc

Feature-Rich Audio Model Inversion for Data-Free Knowledge Distillation Towards General Sound Classification

Data-Free Knowledge Distillation (DFKD) has recently attracted growing attention in the academic community, especially with major breakthroughs in computer vision. Despite promising results, the technique has not been well applied to audio and signal processing. Due to the variable duration of audio signals, it has its own unique way of modeling. In this work, we propose feature-rich audio model inversion (FRAMI), a data-free knowledge distillation framework for general sound classification tasks. It first generates high-quality and feature-rich Mel-spectrograms through a feature-invariant contrastive loss. Then, the hidden states before and after the statistics pooling layer are reused when knowledge distillation is performed on these feature-rich samples. Experimental results on the Urbansound8k, ESC-50, and audioMNIST datasets demonstrate that FRAMI can generate feature-rich samples. Meanwhile, the accuracy of the student model is further improved by reusing the hidden state and significantly outperforms the baseline method.Comment: Accepted by ICASSP 2023. International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023

GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection

Detecting out-of-distribution (OOD) examples is crucial to guarantee the reliability and safety of deep neural networks in real-world settings. In this paper, we offer an innovative perspective on quantifying the disparities between in-distribution (ID) and OOD data -- analyzing the uncertainty that arises when models attempt to explain their predictive decisions. This perspective is motivated by our observation that gradient-based attribution methods encounter challenges in assigning feature importance to OOD data, thereby yielding divergent explanation patterns. Consequently, we investigate how attribution gradients lead to uncertain explanation outcomes and introduce two forms of abnormalities for OOD detection: the zero-deflation abnormality and the channel-wise average abnormality. We then propose GAIA, a simple and effective approach that incorporates Gradient Abnormality Inspection and Aggregation. The effectiveness of GAIA is validated on both commonly utilized (CIFAR) and large-scale (ImageNet-1k) benchmarks. Specifically, GAIA reduces the average FPR95 by 23.10% on CIFAR10 and by 45.41% on CIFAR100 compared to advanced post-hoc methods.Comment: Accepted by NeurIPS202

Adaptive Sparse and Monotonic Attention for Transformer-based Automatic Speech Recognition

The Transformer architecture model, based on self-attention and multi-head attention, has achieved remarkable success in offline end-to-end Automatic Speech Recognition (ASR). However, self-attention and multi-head attention cannot be easily applied for streaming or online ASR. For self-attention in Transformer ASR, the softmax normalization function-based attention mechanism makes it impossible to highlight important speech information. For multi-head attention in Transformer ASR, it is not easy to model monotonic alignments in different heads. To overcome these two limits, we integrate sparse attention and monotonic attention into Transformer-based ASR. The sparse mechanism introduces a learned sparsity scheme to enable each self-attention structure to fit the corresponding head better. The monotonic attention deploys regularization to prune redundant heads for the multi-head attention structure. The experiments show that our method can effectively improve the attention mechanism on widely used benchmarks of speech recognition.Comment: Accepted to DSAA 202

Effect of Bushen yixue decoction on follicular development in experimental androgen-sterilized anovulatory rats and its possible mechanism of action

Purpose: To explore the activities of Bushen yixue decoction (BSY) against follicular development in anovulatory rats.Methods: Rats were divided into normal, normal control, clomifene citrate (positive control, orally, 5 mg/kg), and BSY (orally, 50, 100, 200 mg/kg) groups. Anovulatory rats were prepared by testosterone propionate injection (1.5 mg/rat). After 70 days, daily vaginal smears were performed for 10 days until no obvious sexual cycle was observed, indicating that androgen-sterilized anovulatory rats were successfully established. High performance liquid chromatography (HPLC) was used to analyse BSY chemical composition. Levels of follicular stimulating hormone (FSH), luteinizing hormone (LH), oestradiol (E2), progesterone (P), prolactin (PRL), inhibin (INH), activin (ACT) and follistatin (FS) were determined by radioimmunoassay or enzyme linked immunosorbent assay (ELISA). Western blotting was used to determine Bcl-2, cleaved-caspase-3, Bax, MMP-9 and VEGF in ovarian tissues.Results: BSY increased (p < 0.05) the levels of FSH, LH, E2 (p < 0.05) and ACT, but decreased (p < 0.05) the levels of PRL, INH and FS, relative to control rats. Expressions of VEGF (p < 0.01), MMP-9 (p < 0.05) and Bcl-2 (p < 0.01) were up-regulated by BSY, whereas Bax (p < 0.01) and C-caspase-3 (p < 0.01) were down-regulated.Conclusion: BSY promotes follicular development of anovulatory rats via regulating INH-ACT-FS hormones, VEGF, MMP-9, Caspase-3, Bax, and Bcl-2. Thus, BSY may have the potential to be developed for clinical management of infertility.Keywords: Bushen yixue decoction, Follicular development, Inhibin-Activin-Follistatin (INH-ACT-FS) system, Androgen-sterilized anovulatory rat

Polymorph of 4-(carbazol-9-yl)benzo­nitrile

The asymmetric unit of the title compound, C19H12N2, contains two independent mol­ecules with a similar structure. In the two mol­ecules, the dihedral angles between the carbazole ring system and the benzene ring are 47.9 (5) and 45.4 (4)°, similar to the value of 47.89 (6)° found in the previously reported structure [Saha & Samanta (1999 ▶). Acta Cryst. C55, 1299–1300]. In the crystal, there is a weak C—H⋯N hydrogen bond between the two independent mol­ecules