Vision Transformers Versus Convolutional Neural Networks: Comparing Robustness by Exploiting Varying Local Features
Bibliographic record
Abstract
Robustness of deep learning models is a crucial point that should be evaluated in computer vision tasks. Deep learning models must be able to produce the same results under different conditions such as brightness, image rotation, etc. Deep learning models must have high level robustness to be reliable to address real world problems. Unreliable systems may be harmful for critical systems such as security, healthcare, and military. In this study, it is aimed to evaluate image rotation robustness of Convolutional Neural Networks (CNNs) and Transformer-based deep learning models: MobileNetV2, Residual Network 18 (ResNet18), Visual Geometry Group 16 (VGG16), EfficientNetB1, Vision Transformer (ViT), Data-Efficient Image Transformers (DeiT), and Pooling-based Vision Transformer (PiT). Canadian Institute for Advanced Research 100 classes (CIFAR100), Canadian Institute for Advanced Research 10 classes (CIFAR10) and California Institute of Technology 256 classes (Caltech256) datasets were used to evaluate the performance of the models. All deep learning models were trained with original train datasets. Image embeddings of train, test datasets, and their rotated forms were extracted. A Support Vector Classifier (SVC) was trained with embeddings of original dataset and concatenation of original and rotated form of train dataset. Performances on original test dataset and their rotated forms were evaluated. 10, 50, and 90 degrees rotated form of the datasets were used for rotated test dataset. F1-score metrics were used compare model performances. In conclusion, Transformer-based classification algorithms are more robust to image rotation than the CNN-based models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".