{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/denoising/papers/56","list_of":"/task/denoising","task":"Denoising","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":56,"pages_in_order":73,"rows_per_page":100,"rows":[5501,5600],"of":7282,"counts":{"archive_papers_tagged":7282,"with_a_code_link":2838,"where_syntology_ran_a_sample":832,"not_listed_spam_title":0,"listed":7282,"listed_where_code_ran":832,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":720,"every_run_a_failure_of_syntologys_instrument":112,"listed_with_a_run_with_no_instrument_failure":720,"listed_every_run_a_failure_of_syntologys_instrument":112,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/denoising","prev":"/task/denoising/papers/55","next":"/task/denoising/papers/57","papers":[{"url":null,"slug":"shallow-camera-pipeline-for-night-photography","title":"Shallow camera pipeline for night photography rendering","date":"2022-04-19","arxiv_id":"2204.08972","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-channel-speech-dereverberation-using","title":"Speech Dereverberation with A Reverberation Time Shortening Target","date":"2022-04-19","arxiv_id":"2204.08765","repositories_listed":0,"syntology":null},{"url":null,"slug":"metro-efficient-denoising-pretraining-of","title":"METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals","date":"2022-04-13","arxiv_id":"2204.06644","repositories_listed":0,"syntology":null},{"url":null,"slug":"sapinet-a-sparse-event-based-spatiotemporal","title":"Sapinet: A sparse event-based spatiotemporal oscillator for learning in the wild","date":"2022-04-13","arxiv_id":"2204.06216","repositories_listed":0,"syntology":null},{"url":null,"slug":"sumd-super-u-shaped-matrix-decomposition","title":"SUMD: Super U-shaped Matrix Decomposition Convolutional neural network for Image denoising","date":"2022-04-11","arxiv_id":"2204.04861","repositories_listed":0,"syntology":null},{"url":null,"slug":"nan-noise-aware-nerfs-for-burst-denoising","title":"NAN: Noise-Aware NeRFs for Burst-Denoising","date":"2022-04-10","arxiv_id":"2204.04668","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-neural-network-for-news","title":"Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback","date":"2022-04-09","arxiv_id":"2204.04397","repositories_listed":0,"syntology":null},{"url":null,"slug":"motion-artifacts-correction-from-single","title":"Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis","date":"2022-04-09","arxiv_id":"2204.04533","repositories_listed":0,"syntology":null},{"url":"/paper/dancing-under-the-stars-video-denoising-in","slug":"dancing-under-the-stars-video-denoising-in","title":"Dancing under the stars: video denoising in starlight","date":"2022-04-08","arxiv_id":"2204.04210","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-image-denoising-with-a-locally-adaptive","title":"Real Image Denoising With a Locally-Adaptive Bitonic Filter","date":"2022-04-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"underwater-image-enhancement-using-pre","title":"Underwater Image Enhancement Using Pre-trained Transformer","date":"2022-04-08","arxiv_id":"2204.04199","repositories_listed":0,"syntology":null},{"url":null,"slug":"deeptensor-low-rank-tensor-decomposition-with","title":"DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors","date":"2022-04-07","arxiv_id":"2204.03145","repositories_listed":0,"syntology":null},{"url":"/paper/hunyuan-tvr-for-text-video-retrivial","slug":"hunyuan-tvr-for-text-video-retrivial","title":"Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representations","date":"2022-04-07","arxiv_id":"2204.03382","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-dose-ct-denoising-via-sinogram-inner","title":"Low-Dose CT Denoising via Sinogram Inner-Structure Transformer","date":"2022-04-07","arxiv_id":"2204.03163","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-specific-fine-tuning-of-denoising","title":"Domain Specific Fine-tuning of Denoising Sequence-to-Sequence Models for Natural Language Summarization","date":"2022-04-06","arxiv_id":"2204.09716","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-augmented-kalman-filtering-for","title":"Neural Network-augmented Kalman Filtering for Robust Online Speech Dereverberation in Noisy Reverberant Environments","date":"2022-04-06","arxiv_id":"2204.02741","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-denoising-for-microphone","title":"Spectral Denoising for Microphone Classification","date":"2022-04-06","arxiv_id":"2204.02841","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-swiss-army-knife-for-image-to-image","title":"The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models","date":"2022-04-06","arxiv_id":"2204.02641","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-blind-image-denoising-via-implicit","title":"Zero-shot Blind Image Denoising via Implicit Neural Representations","date":"2022-04-05","arxiv_id":"2204.02405","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-fine-grained-noise-model-via","title":"Estimating Fine-Grained Noise Model via Contrastive Learning","date":"2022-04-03","arxiv_id":"2204.01716","repositories_listed":0,"syntology":null},{"url":null,"slug":"restorex-ai-a-contrastive-approach-towards","title":"RestoreX-AI: A Contrastive Approach towards Guiding Image Restoration via Explainable AI Systems","date":"2022-04-03","arxiv_id":"2204.01719","repositories_listed":0,"syntology":null},{"url":null,"slug":"specgrad-diffusion-probabilistic-model-based","title":"SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping","date":"2022-03-31","arxiv_id":"2203.16749","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-equilibrium-assisted-block-sparse-coding","title":"Connections between Deep Equilibrium and Sparse Representation Models with Application to Hyperspectral Image Denoising","date":"2022-03-29","arxiv_id":"2203.15901","repositories_listed":0,"syntology":null},{"url":null,"slug":"synergizing-physics-model-based-and-data","title":"Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey","date":"2022-03-29","arxiv_id":"2203.15725","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-metric-learning-for-transferable","title":"Continuous Metric Learning For Transferable Speech Emotion Recognition and Embedding Across Low-resource Languages","date":"2022-03-28","arxiv_id":"2203.14867","repositories_listed":0,"syntology":null},{"url":null,"slug":"limited-parameter-denoising-for-low-dose-x","title":"Limited Parameter Denoising for Low-dose X-ray Computed Tomography Using Deep Reinforcement Learning","date":"2022-03-28","arxiv_id":"2203.14794","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-inhomogeneous-diffusion-geometry-and","title":"Time-inhomogeneous diffusion geometry and topology","date":"2022-03-28","arxiv_id":"2203.14860","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-transferable-speech-emotion","title":"Towards Transferable Speech Emotion Representation: On loss functions for cross-lingual latent representations","date":"2022-03-28","arxiv_id":"2203.14865","repositories_listed":0,"syntology":null},{"url":null,"slug":"st-fl-style-transfer-preprocessing-in","title":"ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentation","date":"2022-03-25","arxiv_id":"2203.13680","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-kullback-leibler-divergence-between-2","title":"On the Kullback-Leibler divergence between pairwise isotropic Gaussian-Markov random fields","date":"2022-03-24","arxiv_id":"2203.13164","repositories_listed":0,"syntology":null},{"url":null,"slug":"mr-image-denoising-and-super-resolution-using","title":"MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion","date":"2022-03-23","arxiv_id":"2203.12621","repositories_listed":0,"syntology":null},{"url":null,"slug":"progressivemotionseg-mutually-reinforced","title":"ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation","date":"2022-03-22","arxiv_id":"2203.11732","repositories_listed":0,"syntology":null},{"url":null,"slug":"crispnet-color-rendition-isp-net","title":"CRISPnet: Color Rendition ISP Net","date":"2022-03-20","arxiv_id":"2203.10562","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-defense-via-image-denoising-with","title":"Adversarial Defense via Image Denoising with Chaotic Encryption","date":"2022-03-19","arxiv_id":"2203.10290","repositories_listed":0,"syntology":null},{"url":null,"slug":"optical-fiber-fault-detection-and","title":"Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory","date":"2022-03-19","arxiv_id":"2203.12604","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-attacks-on-deep-learning-based-3","title":"RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression","date":"2022-03-18","arxiv_id":"2203.10183","repositories_listed":0,"syntology":null},{"url":null,"slug":"du-vlg-unifying-vision-and-language","title":"DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training","date":"2022-03-17","arxiv_id":"2203.09052","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-compression-based-feature-learning-for","title":"Neural Compression-Based Feature Learning for Video Restoration","date":"2022-03-17","arxiv_id":"2203.09208","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-noise-level-aware-framework-for-pet-image","title":"A Noise-level-aware Framework for PET Image Denoising","date":"2022-03-15","arxiv_id":"2203.08034","repositories_listed":0,"syntology":null},{"url":null,"slug":"fb-mstcn-a-full-band-single-channel-speech","title":"FB-MSTCN: A Full-Band Single-Channel Speech Enhancement Method Based on Multi-Scale Temporal Convolutional Network","date":"2022-03-15","arxiv_id":"2203.07684","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-filtering-over-expanding-graphs","title":"Graph filtering over expanding graphs","date":"2022-03-15","arxiv_id":"2203.08058","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-time-vertex-fractional-fourier","title":"Joint Time-Vertex Fractional Fourier Transform","date":"2022-03-15","arxiv_id":"2203.07655","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-image-denoising-of-pressure","title":"Time-series image denoising of pressure-sensitive paint data by projected multivariate singular spectrum analysis","date":"2022-03-15","arxiv_id":"2203.07574","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-and-feature-extraction-in","title":"Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks","date":"2022-03-14","arxiv_id":"2203.07537","repositories_listed":0,"syntology":null},{"url":null,"slug":"change-detection-from-synthetic-aperture-1","title":"Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network","date":"2022-03-13","arxiv_id":"2203.06543","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-reparametrized-variational-generative","title":"Dual reparametrized Variational Generative Model for Time-Series Forecasting","date":"2022-03-11","arxiv_id":"2203.05766","repositories_listed":0,"syntology":null},{"url":null,"slug":"manifold-modeling-in-quotient-space-learning","title":"Manifold Modeling in Quotient Space: Learning An Invariant Mapping with Decodability of Image Patches","date":"2022-03-10","arxiv_id":"2203.05134","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-dual-output-diffusion-models","title":"Dynamic Dual-Output Diffusion Models","date":"2022-03-08","arxiv_id":"2203.04304","repositories_listed":0,"syntology":null},{"url":null,"slug":"compression-of-user-generated-content-using","title":"Compression of user generated content using denoised references","date":"2022-03-07","arxiv_id":"2203.03553","repositories_listed":0,"syntology":null},{"url":null,"slug":"weighted-mean-and-median-graph-filters-with","title":"Weighted Mean and Median graph Filters with Attenuation Factor for Sensor Network","date":"2022-03-05","arxiv_id":"2203.02741","repositories_listed":0,"syntology":null},{"url":null,"slug":"geodesic-gramian-denoising-applied-to-the","title":"Geodesic Gramian Denoising Applied to the Images Contaminated With Noise Sampled From Diverse Probability Distributions","date":"2022-03-04","arxiv_id":"2203.02600","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-noise-immune-time-domain-inversion-via","title":"High Noise Immune Time-domain Inversion via Cascade Network (TICaN) for Complex Scatterers","date":"2022-03-03","arxiv_id":"2203.04402","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-the-limited-performance-of-a","title":"Investigating the limited performance of a deep-learning-based SPECT denoising approach: An observer-study-based characterization","date":"2022-03-03","arxiv_id":"2203.01918","repositories_listed":0,"syntology":null},{"url":null,"slug":"nuq-a-noise-metric-for-diffusion-mri-via","title":"NUQ: A Noise Metric for Diffusion MRI via Uncertainty Discrepancy Quantification","date":"2022-03-03","arxiv_id":"2203.01921","repositories_listed":0,"syntology":null},{"url":null,"slug":"beam-shape-effects-and-noise-removal-from-thz","title":"Beam-Shape Effects and Noise Removal from THz Time-Domain Images in Reflection Geometry in the 0.25-6 THz Range","date":"2022-03-01","arxiv_id":"2203.00417","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-reconstruction-algorithms-in-radio","title":"Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers","date":"2022-02-25","arxiv_id":"2202.12959","repositories_listed":0,"syntology":null},{"url":null,"slug":"prnu-emphasis-a-generalization-of-the","title":"PRNU Emphasis: a Generalization of the Multiplicative Model","date":"2022-02-24","arxiv_id":"2202.12357","repositories_listed":0,"syntology":null},{"url":null,"slug":"does-prior-knowledge-in-the-form-of-multiple","title":"Does prior knowledge in the form of multiple low-dose PET images (at different dose levels) improve standard-dose PET prediction?","date":"2022-02-22","arxiv_id":"2202.10998","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-pcg-aiid-system-for-l3das22-challenge","title":"The PCG-AIID System for L3DAS22 Challenge: MIMO and MISO convolutional recurrent Network for Multi Channel Speech Enhancement and Speech Recognition","date":"2022-02-21","arxiv_id":"2202.10017","repositories_listed":0,"syntology":null},{"url":null,"slug":"enabling-sub-thz-cloud-rans-distributed","title":"Distributed Machine-Learning for Early HARQ Feedback Prediction in Cloud RANs","date":"2022-02-17","arxiv_id":"2202.08706","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-channel-speech-denoising-for-machine","title":"Multi-Channel Speech Denoising for Machine Ears","date":"2022-02-17","arxiv_id":"2202.08793","repositories_listed":0,"syntology":null},{"url":null,"slug":"texture-aware-autoencoder-pre-training-and","title":"Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition","date":"2022-02-15","arxiv_id":"2202.07499","repositories_listed":0,"syntology":null},{"url":null,"slug":"grasp-and-lift-detection-from-eeg-signal","title":"Grasp-and-Lift Detection from EEG Signal Using Convolutional Neural Network","date":"2022-02-12","arxiv_id":"2202.06128","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-evolution-of-the-covid19-pandemic","title":"Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling","date":"2022-02-11","arxiv_id":"2202.05497","repositories_listed":0,"syntology":null},{"url":null,"slug":"monotonically-convergent-regularization-by","title":"Monotonically Convergent Regularization by Denoising","date":"2022-02-10","arxiv_id":"2202.04961","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-gan-based-denoising-method-for-chinese","title":"A GAN-based Denoising Method for Chinese Stele and Rubbing Calligraphic Image","date":"2022-02-09","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-redundancy-in-the-bottleneck","title":"Reducing Redundancy in the Bottleneck Representation of the Autoencoders","date":"2022-02-09","arxiv_id":"2202.04629","repositories_listed":0,"syntology":null},{"url":null,"slug":"infergrad-improving-diffusion-models-for","title":"InferGrad: Improving Diffusion Models for Vocoder by Considering Inference in Training","date":"2022-02-08","arxiv_id":"2202.03751","repositories_listed":0,"syntology":null},{"url":null,"slug":"sud-supervision-by-denoising-for-medical","title":"Supervision by Denoising for Medical Image Segmentation","date":"2022-02-07","arxiv_id":"2202.02952","repositories_listed":0,"syntology":null},{"url":null,"slug":"bregman-plug-and-play-priors","title":"Bregman Plug-and-Play Priors","date":"2022-02-04","arxiv_id":"2202.02388","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparge-sparse-coding-based-patient-similarity","title":"SparGE: Sparse Coding-based Patient Similarity Learning via Low-rank Constraints and Graph Embedding","date":"2022-02-03","arxiv_id":"2202.01427","repositories_listed":0,"syntology":null},{"url":null,"slug":"error-correction-in-asr-using-sequence-to","title":"Error Correction in ASR using Sequence-to-Sequence Models","date":"2022-02-02","arxiv_id":"2202.01157","repositories_listed":0,"syntology":null},{"url":null,"slug":"practical-noise-simulation-for-rgb-images","title":"Practical Noise Simulation for RGB Images","date":"2022-01-30","arxiv_id":"2201.12773","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-recovery-based-on-tensor-equivalent","title":"Low-Rank Tensor Completion Based on Bivariate Equivalent Minimax-Concave Penalty","date":"2022-01-30","arxiv_id":"2201.12709","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-priori-denoising-strategies-for-sparse","title":"A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study","date":"2022-01-29","arxiv_id":"2201.12683","repositories_listed":0,"syntology":null},{"url":null,"slug":"validation-and-generalizability-of-self","title":"Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI","date":"2022-01-29","arxiv_id":"2201.12535","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-ecg-signal-denoising-filter-selection","title":"A novel ECG signal denoising filter selection algorithm based on conventional neural networks","date":"2022-01-28","arxiv_id":"2202.00579","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-atrial-fibrillation-classification","title":"Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network","date":"2022-01-26","arxiv_id":"2202.05177","repositories_listed":0,"syntology":null},{"url":null,"slug":"extending-the-use-of-mdl-for-high-dimensional","title":"Extending the Use of MDL for High-Dimensional Problems: Variable Selection, Robust Fitting, and Additive Modeling","date":"2022-01-26","arxiv_id":"2201.11171","repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-efficient-deep-neural-networks-for","title":"Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigation","date":"2022-01-25","arxiv_id":"2201.10360","repositories_listed":0,"syntology":null},{"url":null,"slug":"fn-net-remove-the-outliers-by-filtering-the","title":"FN-Net:Remove the Outliers by Filtering the Noise","date":"2022-01-23","arxiv_id":"2201.09213","repositories_listed":0,"syntology":null},{"url":null,"slug":"noise-specific-denoising-method-with","title":"Noise-specific denoising method with applications to high-frequency ultrasonic images","date":"2022-01-21","arxiv_id":"2201.08527","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-neural-machine-translation-by-3","title":"Improving Neural Machine Translation by Denoising Training","date":"2022-01-19","arxiv_id":"2201.07365","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-deep-learning-based-on-auto-encoder","title":"Online Deep Learning based on Auto-Encoder","date":"2022-01-19","arxiv_id":"2201.07383","repositories_listed":0,"syntology":null},{"url":null,"slug":"superpixel-pre-segmentation-of-her2-slides","title":"Superpixel Pre-Segmentation of HER2 Slides for Efficient Annotation","date":"2022-01-19","arxiv_id":"2201.07572","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-denoising-and-hdr-for-raw-video","title":"Joint denoising and HDR for RAW video sequences","date":"2022-01-18","arxiv_id":"2201.07066","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-for-spherical","title":"Convolutional Neural Networks for Spherical Signal Processing via Spherical Haar Tight Framelets","date":"2022-01-17","arxiv_id":"2201.07890","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-models-using-a-single-equation","title":"Diffusion Models Using a Single Equation","date":"2022-01-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-the-quality-control-of-seismic-data","title":"Improving the quality control of seismic data through active learning","date":"2022-01-17","arxiv_id":"2201.06616","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-wave-propagation-with-attention","title":"Predicting waves in fluids with deep neural network","date":"2022-01-17","arxiv_id":"2201.06628","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-enhances-algorithms-for","title":"Machine Learning Enhances Algorithms for Quantifying Non-Equilibrium Dynamics in Correlation Spectroscopy Experiments to Reach Frame-Rate-Limited Time Resolution","date":"2022-01-17","arxiv_id":"2201.07889","repositories_listed":0,"syntology":null},{"url":null,"slug":"ala-adversarial-lightness-attack-via","title":"ALA: Naturalness-aware Adversarial Lightness Attack","date":"2022-01-16","arxiv_id":"2201.06070","repositories_listed":0,"syntology":null},{"url":null,"slug":"balancing-the-style-content-trade-off-in","title":"Balancing the Style-Content Trade-Off in Sentiment Transfer UsingPolarity-Aware Denoising","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-denoising-entity-pre-training-for-neural-1","title":"DEEP: DEnoising Entity Pre-training for Neural Machine Translation","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-maximum-a-posteriori-estimation-with-plug","title":"On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent","date":"2022-01-16","arxiv_id":"2201.06133","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-for-resource","title":"Representation Learning for Resource-Constrained Keyphrase Generation","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"s5-framework-a-review-of-self-supervised","title":"S5 Framework: A Review of Self-Supervised Shared Semantic Space Optimization for Multimodal Zero-Shot Learning","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ulf-cross-validation-for-weak-supervision","title":"ULF: Cross-Validation for Weak Supervision","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-adversarially-robust-deep-image","title":"Towards Adversarially Robust Deep Image Denoising","date":"2022-01-12","arxiv_id":"2201.04397","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-introduction-to-autoencoders","title":"An Introduction to Autoencoders","date":"2022-01-11","arxiv_id":"2201.03898","repositories_listed":0,"syntology":null}],"record_sha256":"c24671269fae529f935585c7b08e8f281334d95a0bd6c8fa35e3018d363cc1cb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}