{"list":"other","total":53,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1905.11946","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","created":"2019-05-28","cited_by_count":6442},{"id":"2201.03545","title":"A ConvNet for the 2020s","created":"2022-01-10","cited_by_count":3579},{"id":"1512.04150","title":"Learning Deep Features for Discriminative Localization","created":"2015-12-14","cited_by_count":2436},{"id":"1610.02357","title":"Xception: Deep Learning with Depthwise Separable Convolutions","created":"2016-10-07","cited_by_count":2332},{"id":"2004.07780","title":"Shortcut Learning in Deep Neural Networks","created":"2020-04-16","cited_by_count":1969},{"id":"1901.08971","title":"FaceForensics++: Learning to Detect Manipulated Facial Images","created":"2019-01-25","cited_by_count":1101},{"id":"2007.14808","title":"Face2Face: Real-time Face Capture and Reenactment of RGB Videos","created":"2020-07-29","cited_by_count":632},{"id":"1604.03540","title":"Training Region-based Object Detectors with Online Hard Example Mining","created":"2016-04-12","cited_by_count":579},{"id":"1909.12962","title":"Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics","created":"2019-09-27","cited_by_count":575},{"id":"2004.05498","title":"FDA: Fourier Domain Adaptation for Semantic Segmentation","created":"2020-04-11","cited_by_count":541},{"id":"2007.09355","title":"Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues","created":"2020-07-18","cited_by_count":523},{"id":"1912.13458","title":"Face X-ray for More General Face Forgery Detection","created":"2019-12-31","cited_by_count":470},{"id":"1809.00888","title":"MesoNet: a Compact Facial Video Forgery Detection Network","created":"2018-09-04","cited_by_count":409},{"id":"2204.08376","title":"Detecting Deepfakes with Self-Blended Images","created":"2022-04-18","cited_by_count":338},{"id":"2105.11120","title":"A Fourier-based Framework for Domain Generalization","created":"2021-05-24","cited_by_count":290},{"id":"2012.07657","title":"Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection","created":"2020-12-14","cited_by_count":287},{"id":"1910.01717","title":"On the Detection of Digital Face Manipulation","created":"2019-10-03","cited_by_count":260},{"id":"2001.03024","title":"DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection","created":"2020-01-09","cited_by_count":254},{"id":"2103.01856","title":"Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain","created":"2021-03-02","cited_by_count":244},{"id":"1701.02096","title":"Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis","created":"2017-01-09","cited_by_count":215},{"id":"2201.00299","title":"Improving Out-of-Distribution Robustness via Selective Augmentation","created":"2022-01-02","cited_by_count":202},{"id":"1810.11215","title":"Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos","created":"2018-10-26","cited_by_count":180},{"id":"2304.13949","title":"UCF: Uncovering Common Features for Generalizable Deepfake Detection","created":"2023-04-27","cited_by_count":180},{"id":"2203.12208","title":"Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection","created":"2022-03-23","cited_by_count":173},{"id":"2307.01426","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","created":"2023-07-04","cited_by_count":166},{"id":"2103.02370","title":"FSDR: Frequency Space Domain Randomization for Domain Generalization","created":"2021-03-03","cited_by_count":164},{"id":"2103.09096","title":"Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection","created":"2021-03-16","cited_by_count":134},{"id":"2201.07131","title":"Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection","created":"2022-01-18","cited_by_count":130},{"id":"2311.11278","title":"Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection","created":"2023-11-19","cited_by_count":127},{"id":"2210.14457","title":"Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization","created":"2022-10-26","cited_by_count":123},{"id":"2112.13522","title":"Dual Contrastive Learning for General Face Forgery Detection","created":"2021-12-27","cited_by_count":100},{"id":"2206.02749","title":"CORE: Consistent Representation Learning for Face Forgery Detection","created":"2022-06-06","cited_by_count":74},{"id":"2403.06592","title":"Exploiting Style Latent Flows for Generalizing Deepfake Video Detection","created":"2024-03-11","cited_by_count":59},{"id":"2211.11296","title":"SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes","created":"2022-11-21","cited_by_count":49},{"id":"2408.17052","title":"Can We Leave Deepfake Data Behind in Training Deepfake Detector?","created":"2024-08-30","cited_by_count":49},{"id":"1706.04264","title":"von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification","created":"2017-06-13","cited_by_count":46},{"id":"2207.09202","title":"Exploring Disentangled Content Information for Face Forgery Detection","created":"2022-07-19","cited_by_count":36},{"id":"2209.15490","title":"Learning Second Order Local Anomaly for General Face Forgery Detection","created":"2022-09-30","cited_by_count":36},{"id":"2207.09679","title":"Explaining Deepfake Detection by Analysing Image Matching","created":"2022-07-20","cited_by_count":34},{"id":"2407.10550","title":"Learning Natural Consistency Representation for Face Forgery Video Detection","created":"2024-07-15","cited_by_count":26},{"id":"2307.14039","title":"Controllable Guide-Space for Generalizable Face Forgery Detection","created":"2023-07-26","cited_by_count":21},{"id":"2404.01509","title":"Can Biases in ImageNet Models Explain Generalization?","created":"2024-04-01","cited_by_count":20},{"id":"2207.03784","title":"A Non-isotropic Probabilistic Take on Proxy-based Deep Metric Learning","created":"2022-07-08","cited_by_count":19},{"id":"2210.01302","title":"Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation","created":"2022-10-04","cited_by_count":18},{"id":"2208.10043","title":"Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-tailed Learning","created":"2022-08-22","cited_by_count":17},{"id":"2403.06748","title":"Shortcut Learning in Medical Image Segmentation","created":"2024-03-11","cited_by_count":16},{"id":"2203.14932","title":"Attributable Visual Similarity Learning","created":"2022-03-28","cited_by_count":12},{"id":"2307.11823","title":"HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness","created":"2023-07-21","cited_by_count":12},{"id":"2408.06779","title":"ED$^4$: Explicit Data-level Debiasing for Deepfake Detection","created":"2024-08-13","cited_by_count":8},{"id":"2303.02034","title":"Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies","created":"2023-03-03","cited_by_count":7},{"id":"2103.14162","title":"Few-shot Weakly-Supervised Object Detection via Directional Statistics","created":"2021-03-25","cited_by_count":4},{"id":"2209.09844","title":"Frequency Dropout: Feature-Level Regularization via Randomized Filtering","created":"2022-09-20","cited_by_count":2}],"next_cursor":null}