{"list":"other","total":58,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"2105.05233","title":"Diffusion Models Beat GANs on Image Synthesis","created":"2021-05-11","cited_by_count":8255},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"1502.03167","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","created":"2015-02-11","cited_by_count":7005},{"id":"2207.12598","title":"Classifier-Free Diffusion Guidance","created":"2022-07-26","cited_by_count":5847},{"id":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5183},{"id":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"1907.05600","title":"Generative Modeling by Estimating Gradients of the Data Distribution","created":"2019-07-12","cited_by_count":4101},{"id":"2201.03545","title":"A ConvNet for the 2020s","created":"2022-01-10","cited_by_count":3579},{"id":"1905.04899","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","created":"2019-05-13","cited_by_count":2884},{"id":"1903.12261","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","created":"2019-03-28","cited_by_count":2877},{"id":"1611.03530","title":"Understanding deep learning requires rethinking generalization","created":"2016-11-10","cited_by_count":1674},{"id":"2111.09881","title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","created":"2021-11-18","cited_by_count":1627},{"id":"1803.08494","title":"Group Normalization","created":"2018-03-22","cited_by_count":1524},{"id":"1608.03981","title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising","created":"2016-08-13","cited_by_count":1358},{"id":"1902.02918","title":"Certified Adversarial Robustness via Randomized Smoothing","created":"2019-02-08","cited_by_count":1340},{"id":"1911.04252","title":"Self-training with Noisy Student improves ImageNet classification","created":"2019-11-11","cited_by_count":1311},{"id":"2401.10166","title":"VMamba: Visual State Space Model","created":"2024-01-18","cited_by_count":969},{"id":"2301.13826","title":"Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models","created":"2023-01-31","cited_by_count":702},{"id":"2101.09258","title":"Maximum Likelihood Training of Score-Based Diffusion Models","created":"2021-01-22","cited_by_count":672},{"id":"1710.04026","title":"FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising","created":"2017-10-11","cited_by_count":510},{"id":"2008.13751","title":"Plug-and-Play Image Restoration with Deep Denoiser Prior","created":"2020-08-31","cited_by_count":470},{"id":"1802.03471","title":"Certified Robustness to Adversarial Examples with Differential Privacy","created":"2018-02-09","cited_by_count":468},{"id":"1912.03263","title":"Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One","created":"2019-12-06","cited_by_count":405},{"id":"2110.00476","title":"ResNet strikes back: An improved training procedure in timm","created":"2021-10-01","cited_by_count":368},{"id":"2303.16203","title":"Your Diffusion Model is Secretly a Zero-Shot Classifier","created":"2023-03-28","cited_by_count":306},{"id":"1710.10766","title":"PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples","created":"2017-10-30","cited_by_count":244},{"id":"2310.02557","title":"Generalization in diffusion models arises from geometry-adaptive harmonic representations","created":"2023-10-04","cited_by_count":185},{"id":"1809.03113","title":"Certified Adversarial Robustness with Additive Noise","created":"2018-09-10","cited_by_count":180},{"id":"2306.08877","title":"Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment","created":"2023-06-15","cited_by_count":171},{"id":"1412.5068","title":"Towards Deep Neural Network Architectures Robust to Adversarial Examples","created":"2014-12-11","cited_by_count":146},{"id":"2303.15233","title":"Text-to-Image Diffusion Models are Zero-Shot Classifiers","created":"2023-03-27","cited_by_count":143},{"id":"1705.02498","title":"A Study and Comparison of Human and Deep Learning Recognition Performance Under Visual Distortions","created":"2017-05-06","cited_by_count":108},{"id":"2110.03220","title":"Gradient Step Denoiser for convergent Plug-and-Play","created":"2021-10-07","cited_by_count":108},{"id":"1906.05478","title":"Robust and interpretable blind image denoising via bias-free convolutional neural networks","created":"2019-06-13","cited_by_count":90},{"id":"2010.04230","title":"No MCMC for me: Amortized sampling for fast and stable training of energy-based models","created":"2020-10-08","cited_by_count":64},{"id":"2210.00726","title":"Statistical Efficiency of Score Matching: The View from Isoperimetry","created":"2022-10-03","cited_by_count":64},{"id":"2111.11010","title":"Density Ratio Estimation via Infinitesimal Classification","created":"2021-11-22","cited_by_count":61},{"id":"2005.03337","title":"Wavelet Integrated CNNs for Noise-Robust Image Classification","created":"2020-05-07","cited_by_count":60},{"id":"2208.00005","title":"Testing Relational Understanding in Text-Guided Image Generation","created":"2022-07-29","cited_by_count":59},{"id":"2309.16779","title":"Intriguing properties of generative classifiers","created":"2023-09-28","cited_by_count":52},{"id":"2210.10606","title":"DALLE-2 is Seeing Double: Flaws in Word-to-Concept Mapping in Text2Image Models","created":"2022-10-19","cited_by_count":51},{"id":"1711.08478","title":"MagNet and \"Efficient Defenses Against Adversarial Attacks\" are Not Robust to Adversarial Examples","created":"2017-11-22","cited_by_count":46},{"id":"2303.06440","title":"Xformer: Hybrid X-Shaped Transformer for Image Denoising","created":"2023-03-11","cited_by_count":37},{"id":"2109.09032","title":"JEM++: Improved Techniques for Training JEM","created":"2021-09-19","cited_by_count":28},{"id":"2007.09070","title":"Hybrid Discriminative-Generative Training via Contrastive Learning","created":"2020-07-17","cited_by_count":24},{"id":"2402.03845","title":"On gauge freedom, conservativity and intrinsic dimensionality estimation in diffusion models","created":"2024-02-06","cited_by_count":23},{"id":"1910.12744","title":"On approximating $\\nabla f$ with neural networks","created":"2019-10-28","cited_by_count":18},{"id":"2209.12753","title":"On Investigating the Conservative Property of Score-Based Generative Models","created":"2022-09-26","cited_by_count":16},{"id":"2304.02012","title":"EGC: Image Generation and Classification via a Diffusion Energy-Based Model","created":"2023-04-04","cited_by_count":16},{"id":"2209.07959","title":"Towards Bridging the Performance Gaps of Joint Energy-based Models","created":"2022-09-16","cited_by_count":15},{"id":"2204.14187","title":"Randomized Smoothing under Attack: How Good is it in Pratice?","created":"2022-04-28","cited_by_count":7},{"id":"2304.09403","title":"Wavelets Beat Monkeys at Adversarial Robustness","created":"2023-04-19","cited_by_count":2},{"id":"2402.10095","title":"Classification Diffusion Models: Revitalizing Density Ratio Estimation","created":"2024-02-15","cited_by_count":2}],"next_cursor":null}