Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
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 | Venue PDF | LLM Run Details | Input Tokens: 18,969 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,997 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |