Target-Aware Generative Augmentations for Single-Shot Adaptation
Authors: Kowshik Thopalli, Rakshith Subramanyam, Pavan K. Turaga, Jayaraman J. Thiagarajan
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using experiments on a variety of benchmarks, distribution shifts and image corruptions, we find that Si STA produces significantly improved generalization over existing baselines in face attribute detection and multi-class object recognition. Furthermore, Si STA performs competitively to models obtained by training on larger target datasets. We perform an extensive evaluation of Si STA using a suite of classification tasks with multiple benchmark datasets, different Style GAN architectures and more importantly, a variety of challenging distribution shifts. |
| Researcher Affiliation | Collaboration | 1Lawrence Livermore National Laboratory, Livermore, CA, USA 2Arizona State University, Tempe, AZ, USA. Correspondence to: Kowshik Thopalli <thopalli1@llnl.gov>. |
| Pseudocode | Yes | Algorithm 1 Si STA-G |
| Open Source Code | Yes | Our codes can be accessed at https://github. com/Rakshith-2905/Si STA. |
| Open Datasets | Yes | For our empirical study, we consider the following four datasets: (i) Celeb A-HQ (Karras et al., 2017)...; (ii) AFHQ (Choi et al., 2020)...; (iii) CIFAR-10 (Krizhevsky et al., 2009)...; and (iv) Domain Net (Peng et al., 2019)... Image Net (Russakovsky et al., 2015). |
| Dataset Splits | No | The paper mentions 'standard train-test splits' for CIFAR-10 and evaluating on a 'held-out test set', but does not explicitly provide details about a validation dataset split, its size, or how it was used to reproduce the experiment. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types with speeds, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions models (e.g., Style GAN-v2, Res Net-50) and optimizers (Adam, SGD) but does not list specific software dependencies with their version numbers, such as programming language versions or library versions (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | To obtain the source model Fs we fine-tune an Image Net pre-trained Res Net-50 (He et al., 2016) with labeled source data. We use a learning rate of 1e 4, Adam optimizer and train for 30 epochs; (b) Style GAN fine-tuning: We finetune Gs for 300 iterations (M in Algorithm 1) using one-target image with learning rate set to 2e 3 and Adam optimizer with β = 0.99...; (c) Synthetic data curation: The size of the synthetic target dataset Dt, T, was set to 1000 images in all experiments...; (d) Choice of pruning ratio: For all experiments, we set p = 20% for prune-rewind and p = 50% for prune-zero strategies...; (e) SFDA training: For the NRC algorithm, we set both neighborhood and expanded neighborhood sizes at 5 respectively. Finally, we adapt Fs using SGD with momentum 0.9 and learning rate 1e 3. |