Dream the Impossible: Outlier Imagination with Diffusion Models
Authors: Xuefeng Du, Yiyou Sun, Jerry Zhu, Yixuan Li
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive quantitative and qualitative studies to understand the efficacy of DREAM-OOD, and show that training with the samples generated by DREAM-OOD can benefit OOD detection performance. and 4 Experiments and Analysis In this section, we present empirical evidence to validate the effectiveness of our proposed outlier imagination framework. In what follows, we show that DREAM-OOD produces meaningful OOD images, and as a result, significantly improves OOD detection (Section 4.1) performance. |
| Researcher Affiliation | Academia | Xuefeng Du, Yiyou Sun, Xiaojin Zhu, Yixuan Li Department of Computer Sciences University of Wisconsin, Madison {xfdu,sunyiyou,jerryzhu,sharonli}@cs.wisc.edu |
| Pseudocode | Yes | Algorithm 1 DREAM-OOD: Outlier Imagination with Diffusion Models Input: In-distribution training data D = {(xi, yi)}n i=1, initial model parameters θ for learning the text-conditioned latent space, diffusion model. Output: Synthetic images xood. Phases: Phase 1: Learning the Text-conditioned Latent Space. Phase 2: Outlier Imagination via Text-Conditioned Latent. while Phase 1 do 1. Extract token embeddings T (y) of the ID label y Y. 2. Learn the text-conditioned latent representation space by Equation (2). end while Phase 2 do 1. Sample a set of outlier embeddings Vi in the low-likelihood region of the text-conditioned latent space as in Section 3.2. 2. Decode the outlier embeddings into the pixel-space OOD images via diffusion model by Equation (6). end |
| Open Source Code | Yes | Code is publicly available at https://github.com/deeplearning-wisc/dream-ood. |
| Open Datasets | Yes | Following [2], we use the CIFAR-100 and the large-scale IMAGENET dataset [8] as the ID training data. and A Details of datasets Image Net-100. We randomly sample 100 classes from IMAGENET-1K [8] to create IMAGENET-100. The dataset contains the following categories: n01498041, n01514859, n01582220, n01608432, n01616318, n01687978, ... |
| Dataset Splits | No | No explicit specification of train/validation/test dataset splits (percentages or counts) or citations to predefined splits for the ID training data (CIFAR-100, IMAGENET-100). While standard benchmarks often have predefined splits, the paper does not explicitly state which splits were used for their own training/validation process. |
| Hardware Specification | Yes | We run all experiments with Python 3.8.5 and Py Torch 1.13.1, using NVIDIA Ge Force RTX 2080Ti GPUs. |
| Software Dependencies | Yes | We run all experiments with Python 3.8.5 and Py Torch 1.13.1, using NVIDIA Ge Force RTX 2080Ti GPUs. |
| Experiment Setup | Yes | We train the model using stochastic gradient descent for 100 epochs with the cosine learning rate decay schedule, a momentum of 0.9, and a weight decay of 5e 4. The initial learning rate is set to 0.1 and the batch size is set to 160. and β is set to 1.0 for IMAGENET-100 and 2.5 for CIFAR-100. To learn the feature encoder hθ, we set the temperature t in Equation (2) to 0.1. |