TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?
Authors: Yuejiang Liu, Parth Kothari, Bastien van Delft, Baptiste Bellot-Gurlet, Taylor Mordan, Alexandre Alahi
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate that our modified version of test-time training, termed TTT++, outperforms state-of-the-art methods by significant margins on several benchmarks. |
| Researcher Affiliation | Academia | École Polytechnique Fédérale de Lausanne (EPFL){firstname.lastname}@epfl.ch |
| Pseudocode | No | The paper describes the proposed method in prose and through diagrams (Figure 2) but does not provide formal pseudocode or an algorithm block. |
| Open Source Code | Yes | Our code is available at https://github.com/vita-epfl/ttt-plus-plus. |
| Open Datasets | Yes | We train Res Net-50 [38] on CIFAR10/CIFAR100 [39] and test it on the CIFAR10-C/CIFAR100-C [1] datasets, which contain 15 types of algorithmically generated corruptions, such as noise, blur and snow effects. ... We finally validate the effectiveness of our method on the Vis DA-C dataset [42] |
| Dataset Splits | No | The paper discusses batch sizes and queue sizes for test-time adaptation but does not specify training/validation/test splits, only mentions that it trains on CIFAR10/CIFAR100 and tests on their corrupted versions. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions training models like ResNet-50 but does not specify any software dependencies or libraries with version numbers (e.g., PyTorch, TensorFlow, Python versions). |
| Experiment Setup | Yes | We use a batch size of 256 for test-time adaptation. In addition, we use a dynamic queue containing 16 batches of feature vectors for online feature alignment on CIFAR100-C. |