Yosuke Yamaguchi

Soumik Mukhopadhyay, Matthew Gwilliam, Yosuke Yamaguchi et al.

While many unsupervised learning models focus on one family of tasks, either generative or discriminative, we explore the possibility of a unified representation learner: a model which addresses both families of tasks simultaneously. We identify diffusion models, a state-of-the-art method for generative tasks, as a prime candidate. Such models involve training a U-Net to iteratively predict and remove noise, and the resulting model can synthesize high-fidelity, diverse, novel images. We find that the intermediate feature maps of the U-Net are diverse, discriminative feature representations. We propose a novel attention mechanism for pooling feature maps and further leverage this mechanism as DifFormer, a transformer feature fusion of features from different diffusion U-Net blocks and noise steps. We also develop DifFeed, a novel feedback mechanism tailored to diffusion. We find that diffusion models are better than GANs, and, with our fusion and feedback mechanisms, can compete with state-of-the-art unsupervised image representation learning methods for discriminative tasks - image classification with full and semi-supervision, transfer for fine-grained classification, object detection and segmentation, and semantic segmentation. Our project website (https://mgwillia.github.io/diffssl/) and code (https://github.com/soumik-kanad/diffssl) are available publicly.

Yosuke Yamaguchi, Issei Suemitsu, Wenpeng Wei

In practical time series forecasting, covariates provide rich contextual information that can potentially enhance the forecast of target variables. Although some covariates extend into the future forecasting horizon (e.g., calendar events, discount schedules), most multivariate models fail to leverage this pivotal insight due to the length discrepancy with target variables. Additionally, capturing the dependency between target variables and covariates is non-trivial, as models must precisely reflect the local impact of covariates while also capturing global cross-variate dependencies. To overcome these challenges, we propose CITRAS, a decoder-only Transformer that flexibly leverages multiple targets, past covariates, and future covariates. While preserving strong autoregressive capabilities, CITRAS introduces two novel mechanisms in patch-wise cross-variate attention: Key-Value (KV) Shift and Attention Score Smoothing. KV Shift seamlessly incorporates future covariates into the forecasting of target variables based on their concurrent dependencies. Additionally, Attention Score Smoothing refines locally accurate patch-wise cross-variate dependencies into global variate-level dependencies by smoothing the past series of attention scores. Experimentally, CITRAS outperforms state-of-the-art models on thirteen real-world benchmarks from both covariate-informed and multivariate settings, demonstrating its versatile ability to leverage cross-variate and cross-time dependencies for improved forecasting accuracy.