Papers with code for free#
Papers with code download#

This helps to keep your main text focused but still allows you to include the information you want to include somewhere in your paper. While including it all in the body would make your paper too long and unwieldy, this is exactly what an appendix is for.Īs a rule of thumb, any detailed information that is not immediately needed to make your point can go in an appendix. However, after doing all that research, you’ll often find that you have a lot of other interesting information that you want to share with your reader. In the main body of your research paper, it’s important to provide clear and concise information that supports your argument and conclusions.

Frequently asked questions about appendices.

What is an appendix in a research paper?.

Papers with code download#

You can download these in the format of your choice below. We’ve prepared some examples and templates for you, for inclusions such as research protocols, survey questions, and interview transcripts. Appendices are a useful tool for providing additional information or clarification in a research paper, dissertation, or thesis without making your final product too long.Īppendices help you provide more background information and nuance about your thesis or dissertation topic without disrupting your text with too many tables and figures or other distracting elements.

Papers with code for free#

Try for free Research Paper Appendix | Example & TemplatesĪn appendix is a supplementary document that facilitates your reader’s understanding of your research but is not essential to your core argument. Notably, DeepFake-Adapter demonstrates a convincing advantage under cross-dataset and cross-manipulation settings.Eliminate grammar errors and improve your writing with our free AI-powered grammar checker. Extensive experiments on several standard deepfake detection benchmarks validate the effectiveness of our approach. Unlike existing deepfake detection methods merely focusing on low-level forgery patterns, the forgery detection process of our model can be regularized by generalizable high-level semantics from a pre-trained ViT and adapted by global and local low-level forgeries of deepfake data. Specifically, to guide the adaptation process to be aware of both global and local forgery cues of deepfake data, 1) we not only insert Globally-aware Bottleneck Adapters in parallel to MLP layers of ViT, 2) but also actively cross-attend Locally-aware Spatial Adapters with features from ViT. Given large pre-trained models but limited deepfake data, DeepFake-Adapter introduces lightweight yet dedicated dual-level adapter modules to a ViT while keeping the model backbone frozen. In this paper, we propose the first parameter-efficient tuning approach for deepfake detection, namely DeepFake-Adapter, to effectively and efficiently adapt the generalizable high-level semantics from large pre-trained ViTs to aid deepfake detection. Recently, large pre-trained Vision Transformers (ViTs) have shown promising generalization capability. Here we argue that high-level semantics are also indispensable recipes for generalizable forgery detection.

$papers with code$

Existing deepfake detection methods fail to generalize well to unseen or degraded samples, which can be attributed to the over-fitting of low-level forgery patterns.