6 research outputs found
Multi-Architecture Multi-Expert Diffusion Models
Diffusion models have achieved impressive results in generating diverse and
realistic data by employing multi-step denoising processes. However, the need
for accommodating significant variations in input noise at each time-step has
led to diffusion models requiring a large number of parameters for their
denoisers. We have observed that diffusion models effectively act as filters
for different frequency ranges at each time-step noise. While some previous
works have introduced multi-expert strategies, assigning denoisers to different
noise intervals, they overlook the importance of specialized operations for
high and low frequencies. For instance, self-attention operations are effective
at handling low-frequency components (low-pass filters), while convolutions
excel at capturing high-frequency features (high-pass filters). In other words,
existing diffusion models employ denoisers with the same architecture, without
considering the optimal operations for each time-step noise. To address this
limitation, we propose a novel approach called Multi-architecturE Multi-Expert
(MEME), which consists of multiple experts with specialized architectures
tailored to the operations required at each time-step interval. Through
extensive experiments, we demonstrate that MEME outperforms large competitors
in terms of both generation performance and computational efficiency
Towards Practical Plug-and-Play Diffusion Models
Diffusion-based generative models have achieved remarkable success in image
generation. Their guidance formulation allows an external model to
plug-and-play control the generation process for various tasks without
fine-tuning the diffusion model. However, the direct use of publicly available
off-the-shelf models for guidance fails due to their poor performance on noisy
inputs. For that, the existing practice is to fine-tune the guidance models
with labeled data corrupted with noises. In this paper, we argue that this
practice has limitations in two aspects: (1) performing on inputs with
extremely various noises is too hard for a single model; (2) collecting labeled
datasets hinders scaling up for various tasks. To tackle the limitations, we
propose a novel strategy that leverages multiple experts where each expert is
specialized in a particular noise range and guides the reverse process at its
corresponding timesteps. However, as it is infeasible to manage multiple
networks and utilize labeled data, we present a practical guidance framework
termed Practical Plug-And-Play (PPAP), which leverages parameter-efficient
fine-tuning and data-free knowledge transfer. We exhaustively conduct ImageNet
class conditional generation experiments to show that our method can
successfully guide diffusion with small trainable parameters and no labeled
data. Finally, we show that image classifiers, depth estimators, and semantic
segmentation models can guide publicly available GLIDE through our framework in
a plug-and-play manner
Determining the influence of ship hull deformations caused by draught change on shaft alignment application using FE analysis
This paper was to address the shortcomings of current design practice to evaluate the stability of the shaft alignment for a 300,000 DWT Very Large Crude Oil Carrier. An enhanced approach using FE was applied to identify the influence of hull deformation on the alignment of the shafting system. The effectiveness of this method was demonstrated in comparison with Jack up technique. Analysis results showed that the hull deformation could be a key factor affecting the offset distortion of each bearing supporting the shaft line. Moreover, it was confirmed that the deformation pattern of cargo hold was opposite to the deformation of engine room structure when hull deformation occurred due to draught change of the case ship. As new research findings, they are believed to contribute significantly to the prevention of shaft damage associated with hull deformations, thereby improving the reliability of shaft alignment for similar types of vessels
C2L: Causally Contrastive Learning for Robust Text Classification
Despite the super-human accuracy of recent deep models in NLP tasks, their robustness is reportedly limited due to their reliance on spurious patterns. We thus aim to leverage contrastive learning and counterfactual augmentation for robustness. For augmentation, existing work either requires humans to add counterfactuals to the dataset or machines to automatically matches near-counterfactuals already in the dataset. Unlike existing augmentation is affected by spurious correlations, ours, by synthesizing “a set” of counterfactuals, and making a collective decision on the distribution of predictions on this set, can robustly supervise the causality of each term. Our empirical results show that our approach, by collective decisions, is less sensitive to task model bias of attribution-based synthesis, and thus achieves significant improvements, in diverse dimensions: 1) counterfactual robustness, 2) cross-domain generalization, and 3) generalization from scarce data
Structure-Augmented Keyphrase Generation
© 2021 Association for Computational LinguisticsThis paper studies the keyphrase generation (KG) task for scenarios where structure plays an important role. For example, a scientific publication consists of a short title and a long body, where the title can be used for de-emphasizing unimportant details in the body. Similarly, for short social media posts (e.g., tweets), scarce context can be augmented from titles, though often missing. Our contribution is generating/augmenting structure then encoding these information, using existing keyphrases of other documents, complementing missing/incomplete titles. Specifically, we first extend the given document with related but absent keyphrases from existing keyphrases, to augment missing contexts (generating structure), and then, build a graph of keyphrases and the given document, to obtain structure-aware representation of the augmented text (encoding structure). Our empirical results validate that our proposed structure augmentation and structure-aware encoding can improve KG for both scenarios, outperforming the state-of-the-art.N
Counterfactual Generative Smoothing for Imbalanced Natural Language Classification
© 2021 ACM.Classification datasets are often biased in observations, leaving onlya few observations for minority classes. Our key contribution is de-tecting and reducing Under-represented (U-) and Over-represented(O-) artifacts from dataset imbalance, by proposing a Counterfac-tual Generative Smoothing approach on both feature-space anddata-space, namely CGS_f and CGS_d. Our technical contribution issmoothing majority and minority observations, by sampling a ma-jority seed and transferring to minority. Our proposed approachesnot only outperform state-of-the-arts in both synthetic and real-lifedatasets, they effectively reduce both artifact types.N