{"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":"/method/residual-block/papers/8","list_of":"/method/residual-block","method":"Residual Block","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":8,"pages_in_order":29,"rows_per_page":100,"rows":[701,800],"of":2807,"counts":{"archive_papers_tagged":2807,"with_a_code_link":1322,"where_syntology_ran_a_sample":375,"not_listed_spam_title":0,"listed":2807,"listed_where_code_ran":375,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":316,"every_run_a_failure_of_syntologys_instrument":59,"listed_with_a_run_with_no_instrument_failure":316,"listed_every_run_a_failure_of_syntologys_instrument":59,"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":"/method/residual-block","prev":"/method/residual-block/papers/7","next":"/method/residual-block/papers/9","papers":[{"paper":"/paper/siamese-nas-using-trained-samples-efficiently","slug":"siamese-nas-using-trained-samples-efficiently","title":"Siamese-NAS: Using Trained Samples Efficiently to Find Lightweight Neural Architecture by Prior Knowledge","date":"2022-10-02","arxiv_id":"2210.00546","n_code_links":1,"syntology":null},{"paper":null,"slug":"kernel-normalized-convolutional-networks-for","title":"Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning","date":"2022-09-30","arxiv_id":"2210.00053","n_code_links":0,"syntology":null},{"paper":"/paper/rethinking-skip-connection-model-as-a","slug":"rethinking-skip-connection-model-as-a","title":"Rethinking skip connection model as a learnable Markov chain","date":"2022-09-30","arxiv_id":"2209.15278","n_code_links":1,"syntology":null},{"paper":"/paper/towards-multi-spatiotemporal-scale","slug":"towards-multi-spatiotemporal-scale","title":"Towards Multi-spatiotemporal-scale Generalized PDE Modeling","date":"2022-09-30","arxiv_id":"2209.15616","n_code_links":2,"syntology":null},{"paper":null,"slug":"where-should-i-spend-my-flops-efficiency","title":"Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods","date":"2022-09-30","arxiv_id":"2209.15589","n_code_links":0,"syntology":null},{"paper":null,"slug":"eihi-net-out-of-distribution-generalization","title":"EiHi Net: Out-of-Distribution Generalization Paradigm","date":"2022-09-29","arxiv_id":"2209.14946","n_code_links":0,"syntology":null},{"paper":null,"slug":"cyclegan-network-for-sheet-metal-welding","title":"Cyclegan Network for Sheet Metal Welding Drawing Translation","date":"2022-09-28","arxiv_id":"2209.14106","n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-prediction-of-spontaneous-humour-a","slug":"multimodal-prediction-of-spontaneous-humour-a","title":"Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results","date":"2022-09-28","arxiv_id":"2209.14272","n_code_links":2,"syntology":null},{"paper":"/paper/recipro-cam-gradient-free-reciprocal-class","slug":"recipro-cam-gradient-free-reciprocal-class","title":"Recipro-CAM: Fast gradient-free visual explanations for convolutional neural networks","date":"2022-09-28","arxiv_id":"2209.14074","n_code_links":1,"syntology":null},{"paper":null,"slug":"identifying-and-extracting-football-features","title":"Identifying and Extracting Football Features from Real-World Media Sources using Only Synthetic Training Data","date":"2022-09-27","arxiv_id":"2209.13254","n_code_links":0,"syntology":null},{"paper":null,"slug":"measuring-overfitting-in-convolutional-neural","title":"Measuring Overfitting in Convolutional Neural Networks using Adversarial Perturbations and Label Noise","date":"2022-09-27","arxiv_id":"2209.13382","n_code_links":0,"syntology":null},{"paper":"/paper/observation-centric-and-central-distance","slug":"observation-centric-and-central-distance","title":"Observation Centric and Central Distance Recovery on Sports Player Tracking","date":"2022-09-27","arxiv_id":"2209.13154","n_code_links":1,"syntology":null},{"paper":"/paper/multi-stage-image-denoising-with-the-wavelet","slug":"multi-stage-image-denoising-with-the-wavelet","title":"Multi-stage image denoising with the wavelet transform","date":"2022-09-26","arxiv_id":"2209.12394","n_code_links":1,"syntology":null},{"paper":null,"slug":"optical-neural-ordinary-differential","title":"Optical Neural Ordinary Differential Equations","date":"2022-09-26","arxiv_id":"2209.12898","n_code_links":0,"syntology":null},{"paper":null,"slug":"development-of-ai-cloud-based-high","title":"Development of AI-cloud based high-sensitivity wireless smart sensor for port structure monitoring","date":"2022-09-24","arxiv_id":"2209.13646","n_code_links":0,"syntology":null},{"paper":null,"slug":"hybrid-multimodal-fusion-for-humor-detection","title":"Hybrid Multimodal Fusion for Humor Detection","date":"2022-09-24","arxiv_id":"2209.11949","n_code_links":0,"syntology":null},{"paper":"/paper/on-efficient-reinforcement-learning-for-full","slug":"on-efficient-reinforcement-learning-for-full","title":"On Efficient Reinforcement Learning for Full-length Game of StarCraft II","date":"2022-09-23","arxiv_id":"2209.11553","n_code_links":2,"syntology":{"ran":8,"of":10,"n_ran_checked":8,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["liuruoze/hiernet-sc2","liuruoze/mini-AlphaStar"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"the-kriston-ai-system-for-the-voxceleb","title":"The Kriston AI System for the VoxCeleb Speaker Recognition Challenge 2022","date":"2022-09-23","arxiv_id":"2209.11433","n_code_links":0,"syntology":null},{"paper":null,"slug":"lamarckian-platform-pushing-the-boundaries-of","title":"Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning towards Asynchronous Commercial Games","date":"2022-09-21","arxiv_id":"2209.10055","n_code_links":0,"syntology":null},{"paper":"/paper/sda-x-net-selective-depth-attention-networks","slug":"sda-x-net-selective-depth-attention-networks","title":"SDA-$x$Net: Selective Depth Attention Networks for Adaptive Multi-scale Feature Representation","date":"2022-09-21","arxiv_id":"2209.10327","n_code_links":1,"syntology":null},{"paper":"/paper/a-dual-cycled-cross-view-transformer-network","slug":"a-dual-cycled-cross-view-transformer-network","title":"A Dual-Cycled Cross-View Transformer Network for Unified Road Layout Estimation and 3D Object Detection in the Bird's-Eye-View","date":"2022-09-19","arxiv_id":"2209.08844","n_code_links":1,"syntology":null},{"paper":null,"slug":"effective-adaptation-in-multi-task-co","title":"Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving","date":"2022-09-19","arxiv_id":"2209.08953","n_code_links":0,"syntology":null},{"paper":"/paper/masked-face-inpainting-through-residual","slug":"masked-face-inpainting-through-residual","title":"Masked Face Inpainting Through Residual Attention UNet","date":"2022-09-19","arxiv_id":"2209.08850","n_code_links":1,"syntology":null},{"paper":"/paper/rvsl-robust-vehicle-similarity-learning-in","slug":"rvsl-robust-vehicle-similarity-learning-in","title":"RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on Semi-supervised Learning","date":"2022-09-18","arxiv_id":"2209.08630","n_code_links":1,"syntology":null},{"paper":null,"slug":"inducing-early-neural-collapse-in-deep-neural","title":"Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks","date":"2022-09-17","arxiv_id":"2209.08378","n_code_links":0,"syntology":null},{"paper":"/paper/human-level-atari-200x-faster","slug":"human-level-atari-200x-faster","title":"Human-level Atari 200x faster","date":"2022-09-15","arxiv_id":"2209.07550","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":0,"n_instrument":1,"unverified":2,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":null,"slug":"on-the-interplay-of-adversarial-robustness","title":"On the interplay of adversarial robustness and architecture components: patches, convolution and attention","date":"2022-09-14","arxiv_id":"2209.06953","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparative-analysis-of-segmentation-and","title":"Comparative analysis of segmentation and generative models for fingerprint retrieval task","date":"2022-09-13","arxiv_id":"2209.06172","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-training-on-multi-instance-gpus","title":"An Analysis of Collocation on GPUs for Deep Learning Training","date":"2022-09-13","arxiv_id":"2209.06018","n_code_links":0,"syntology":null},{"paper":null,"slug":"histoperm-a-permutation-based-view-generation","title":"HistoPerm: A Permutation-Based View Generation Approach for Improving Histopathologic Feature Representation Learning","date":"2022-09-13","arxiv_id":"2209.06185","n_code_links":0,"syntology":null},{"paper":"/paper/git-re-basin-merging-models-modulo","slug":"git-re-basin-merging-models-modulo","title":"Git Re-Basin: Merging Models modulo Permutation Symmetries","date":"2022-09-11","arxiv_id":"2209.04836","n_code_links":3,"syntology":{"ran":6,"of":6,"n_ran_checked":1,"n_instrument":5,"unverified":0,"pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","official":{"repos":["samuela/git-re-basin"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"in-situ-animal-behavior-classification-using","title":"In-situ animal behavior classification using knowledge distillation and fixed-point quantization","date":"2022-09-09","arxiv_id":"2209.04130","n_code_links":0,"syntology":null},{"paper":"/paper/generative-adversarial-super-resolution-at","slug":"generative-adversarial-super-resolution-at","title":"Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation","date":"2022-09-07","arxiv_id":"2209.03355","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-masked-bounding-box-selection-based-resnet","title":"A Masked Bounding-Box Selection Based ResNet Predictor for Text Rotation Prediction","date":"2022-09-06","arxiv_id":"2209.09198","n_code_links":0,"syntology":null},{"paper":null,"slug":"multimodal-contrastive-learning-for-remote","title":"Multimodal contrastive learning for remote sensing tasks","date":"2022-09-06","arxiv_id":"2209.02329","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-of-diabetic-retinopathy-using","title":"Detection of diabetic retinopathy using longitudinal self-supervised learning","date":"2022-09-02","arxiv_id":"2209.00915","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-fourier-convolution-based-remote-sensor","title":"Fast Fourier Convolution Based Remote Sensor Image Object Detection for Earth Observation","date":"2022-09-01","arxiv_id":"2209.00551","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-detection-of-morphing-attacks","title":"On the detection of morphing attacks generated by GANs","date":"2022-09-01","arxiv_id":"2209.00404","n_code_links":0,"syntology":null},{"paper":"/paper/transformers-are-sample-efficient-world","slug":"transformers-are-sample-efficient-world","title":"Transformers are Sample-Efficient World Models","date":"2022-09-01","arxiv_id":"2209.00588","n_code_links":2,"syntology":{"ran":17,"of":26,"n_ran_checked":16,"n_instrument":1,"unverified":9,"pointer_only":26,"phrase":"17 ran (of which 13 constructed an object rather than computing a result; 16 with no instrument failure: 1 honoured, 0 violated, 15 with no contract checked; 1 where Syntology's instrument failed) · 9 unverified","official":{"repos":["eloialonso/iris"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":13,"n_ran_no_instrument_failure":16,"n_unverified":9,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"synthetic-latent-fingerprint-generator","title":"Synthetic Latent Fingerprint Generator","date":"2022-08-29","arxiv_id":"2208.13811","n_code_links":0,"syntology":null},{"paper":"/paper/lossy-image-compression-with-quantized","slug":"lossy-image-compression-with-quantized","title":"Lossy Image Compression with Quantized Hierarchical VAEs","date":"2022-08-27","arxiv_id":"2208.13056","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["duanzhiihao/lossy-vae"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/region-guided-cyclegans-for-stain-transfer-in","slug":"region-guided-cyclegans-for-stain-transfer-in","title":"Region-guided CycleGANs for Stain Transfer in Whole Slide Images","date":"2022-08-26","arxiv_id":"2208.12847","n_code_links":1,"syntology":null},{"paper":null,"slug":"image-augmentation-improves-few-shot","title":"Image augmentation improves few-shot classification performance in plant disease recognition","date":"2022-08-25","arxiv_id":"2208.12613","n_code_links":0,"syntology":null},{"paper":null,"slug":"system-fingerprints-detection-for-deepfake","title":"Audio Deepfake Attribution: An Initial Dataset and Investigation","date":"2022-08-21","arxiv_id":"2208.10489","n_code_links":0,"syntology":null},{"paper":null,"slug":"communication-size-reduction-of-federated","title":"Federated Learning of Neural ODE Models with Different Iteration Counts","date":"2022-08-19","arxiv_id":"2208.09478","n_code_links":0,"syntology":null},{"paper":null,"slug":"dance-style-transfer-with-cross-modal","title":"Dance Style Transfer with Cross-modal Transformer","date":"2022-08-19","arxiv_id":"2208.09406","n_code_links":0,"syntology":null},{"paper":"/paper/learning-spatial-frequency-transformer-for","slug":"learning-spatial-frequency-transformer-for","title":"Learning Spatial-Frequency Transformer for Visual Object Tracking","date":"2022-08-18","arxiv_id":"2208.08829","n_code_links":1,"syntology":null},{"paper":null,"slug":"semi-supervised-domain-adaptation-with-2","title":"Semi-supervised domain adaptation with CycleGAN guided by a downstream task loss","date":"2022-08-18","arxiv_id":"2208.08815","n_code_links":0,"syntology":null},{"paper":null,"slug":"blind-spot-collision-detection-system-for","title":"Blind-Spot Collision Detection System for Commercial Vehicles Using Multi Deep CNN Architecture","date":"2022-08-17","arxiv_id":"2208.08224","n_code_links":0,"syntology":null},{"paper":"/paper/evaluation-of-3d-gans-for-lung-tissue","slug":"evaluation-of-3d-gans-for-lung-tissue","title":"Evaluation of 3D GANs for Lung Tissue Modelling in Pulmonary CT","date":"2022-08-17","arxiv_id":"2208.08184","n_code_links":1,"syntology":null},{"paper":"/paper/video-transunet-temporally-blended-vision","slug":"video-transunet-temporally-blended-vision","title":"Video-TransUNet: Temporally Blended Vision Transformer for CT VFSS Instance Segmentation","date":"2022-08-17","arxiv_id":"2208.08315","n_code_links":2,"syntology":null},{"paper":"/paper/combining-gradients-and-probabilities-for","slug":"combining-gradients-and-probabilities-for","title":"Combining Gradients and Probabilities for Heterogeneous Approximation of Neural Networks","date":"2022-08-15","arxiv_id":"2208.07265","n_code_links":1,"syntology":null},{"paper":null,"slug":"self-supervised-multimodal-fusion-transformer","title":"Self-Supervised Multimodal Fusion Transformer for Passive Activity Recognition","date":"2022-08-15","arxiv_id":"2209.03765","n_code_links":0,"syntology":null},{"paper":"/paper/coshnet-a-hybird-complex-valued-neural","slug":"coshnet-a-hybird-complex-valued-neural","title":"CoShNet: A Hybrid Complex Valued Neural Network using Shearlets","date":"2022-08-14","arxiv_id":"2208.06882","n_code_links":1,"syntology":null},{"paper":"/paper/adan-adaptive-nesterov-momentum-algorithm-for","slug":"adan-adaptive-nesterov-momentum-algorithm-for","title":"Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models","date":"2022-08-13","arxiv_id":"2208.06677","n_code_links":9,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["sail-sg/adan"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"cyclegan-with-three-different-unpaired","title":"CycleGAN with three different unpaired datasets","date":"2022-08-12","arxiv_id":"2208.06526","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-pros-and-cons-of-momentum-encoder-in","title":"On the Pros and Cons of Momentum Encoder in Self-Supervised Visual Representation Learning","date":"2022-08-11","arxiv_id":"2208.05744","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimizing-anchor-based-detectors-for","title":"Optimizing Anchor-based Detectors for Autonomous Driving Scenes","date":"2022-08-11","arxiv_id":"2208.06062","n_code_links":0,"syntology":null},{"paper":"/paper/generative-transfer-learning-covid-19","slug":"generative-transfer-learning-covid-19","title":"Generative Transfer Learning: Covid-19 Classification with a few Chest X-ray Images","date":"2022-08-10","arxiv_id":"2208.05305","n_code_links":1,"syntology":null},{"paper":null,"slug":"res-dense-net-for-3d-covid-chest-ct-scan","title":"Res-Dense Net for 3D Covid Chest CT-scan classification","date":"2022-08-09","arxiv_id":"2208.04613","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-neural-net-approaches-in-metal","title":"Efficient Neural Net Approaches in Metal Casting Defect Detection","date":"2022-08-08","arxiv_id":"2208.04150","n_code_links":0,"syntology":null},{"paper":"/paper/skdcgn-source-free-knowledge-distillation-of","slug":"skdcgn-source-free-knowledge-distillation-of","title":"SKDCGN: Source-free Knowledge Distillation of Counterfactual Generative Networks using cGANs","date":"2022-08-08","arxiv_id":"2208.04226","n_code_links":1,"syntology":null},{"paper":"/paper/no-more-strided-convolutions-or-pooling-a-new","slug":"no-more-strided-convolutions-or-pooling-a-new","title":"No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects","date":"2022-08-07","arxiv_id":"2208.03641","n_code_links":1,"syntology":null},{"paper":null,"slug":"time-frequency-distributions-of-heart-sound","title":"Time-Frequency Distributions of Heart Sound Signals: A Comparative Study using Convolutional Neural Networks","date":"2022-08-05","arxiv_id":"2208.03128","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-driven-attention-and-data-independent","title":"Attention and DCT based Global Context Modeling for Text-independent Speaker Recognition","date":"2022-08-04","arxiv_id":"2208.02778","n_code_links":0,"syntology":null},{"paper":null,"slug":"metadata-enhanced-contrastive-learning-from","title":"Metadata-enhanced contrastive learning from retinal optical coherence tomography images","date":"2022-08-04","arxiv_id":"2208.02529","n_code_links":0,"syntology":null},{"paper":"/paper/insightr-net-interpretable-neural-network-for","slug":"insightr-net-interpretable-neural-network-for","title":"INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical Examples","date":"2022-07-31","arxiv_id":"2208.00457","n_code_links":1,"syntology":null},{"paper":"/paper/glean-generative-latent-bank-for-image-super","slug":"glean-generative-latent-bank-for-image-super","title":"GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond","date":"2022-07-29","arxiv_id":"2207.14812","n_code_links":1,"syntology":{"ran":7,"of":9,"n_ran_checked":7,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["open-mmlab/mmediting"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/optimization-of-artificial-neural-networks","slug":"optimization-of-artificial-neural-networks","title":"Optimization of Artificial Neural Networks models applied to the identification of images of asteroids' resonant arguments","date":"2022-07-28","arxiv_id":"2207.14181","n_code_links":1,"syntology":null},{"paper":"/paper/spot-the-difference-self-supervised-pre","slug":"spot-the-difference-self-supervised-pre","title":"SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation","date":"2022-07-28","arxiv_id":"2207.14315","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"ssbnet-improving-visual-recognition","title":"SSBNet: Improving Visual Recognition Efficiency by Adaptive Sampling","date":"2022-07-23","arxiv_id":"2207.11511","n_code_links":0,"syntology":null},{"paper":"/paper/scale-dependant-layer-for-self-supervised","slug":"scale-dependant-layer-for-self-supervised","title":"Scale dependant layer for self-supervised nuclei encoding","date":"2022-07-22","arxiv_id":"2207.10950","n_code_links":1,"syntology":null},{"paper":"/paper/dc-shadownet-single-image-hard-and-soft-1","slug":"dc-shadownet-single-image-hard-and-soft-1","title":"DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network","date":"2022-07-21","arxiv_id":"2207.10434","n_code_links":1,"syntology":null},{"paper":"/paper/designing-an-illumination-aware-network-for","slug":"designing-an-illumination-aware-network-for","title":"Designing An Illumination-Aware Network for Deep Image Relighting","date":"2022-07-21","arxiv_id":"2207.10582","n_code_links":1,"syntology":null},{"paper":"/paper/unsupervised-night-image-enhancement-when","slug":"unsupervised-night-image-enhancement-when","title":"Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression","date":"2022-07-21","arxiv_id":"2207.10564","n_code_links":1,"syntology":null},{"paper":"/paper/bigcolor-colorization-using-a-generative","slug":"bigcolor-colorization-using-a-generative","title":"BigColor: Colorization using a Generative Color Prior for Natural Images","date":"2022-07-20","arxiv_id":"2207.09685","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["KIMGEONUNG/BigColor"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"robust-landmark-based-stent-tracking-in-x-ray","title":"Robust Landmark-based Stent Tracking in X-ray Fluoroscopy","date":"2022-07-20","arxiv_id":"2207.09933","n_code_links":0,"syntology":null},{"paper":null,"slug":"gafx-a-general-audio-feature-extractor","title":"GAFX: A General Audio Feature eXtractor","date":"2022-07-19","arxiv_id":"2207.09145","n_code_links":0,"syntology":null},{"paper":"/paper/vologan-adversarial-domain-adaptation-for","slug":"vologan-adversarial-domain-adaptation-for","title":"VoloGAN: Adversarial Domain Adaptation for Synthetic Depth Data","date":"2022-07-19","arxiv_id":"2207.09204","n_code_links":1,"syntology":null},{"paper":null,"slug":"capabilities-limitations-and-challenges-of","title":"Capabilities, Limitations and Challenges of Style Transfer with CycleGANs: A Study on Automatic Ring Design Generation","date":"2022-07-18","arxiv_id":"2207.08989","n_code_links":0,"syntology":null},{"paper":null,"slug":"supervised-contrastive-resnet-and-transfer","title":"Supervised Contrastive ResNet and Transfer Learning for the In-vehicle Intrusion Detection System","date":"2022-07-18","arxiv_id":"2207.10814","n_code_links":0,"syntology":null},{"paper":null,"slug":"mdm-visual-explanations-for-neural-networks","title":"MDM: Multiple Dynamic Masks for Visual Explanation of Neural Networks","date":"2022-07-17","arxiv_id":"2207.08046","n_code_links":0,"syntology":null},{"paper":null,"slug":"low-bit-shift-network-for-end-to-end-spoken","title":"Low-bit Shift Network for End-to-End Spoken Language Understanding","date":"2022-07-15","arxiv_id":"2207.07497","n_code_links":0,"syntology":null},{"paper":"/paper/next-vit-next-generation-vision-transformer","slug":"next-vit-next-generation-vision-transformer","title":"Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios","date":"2022-07-12","arxiv_id":"2207.05501","n_code_links":5,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["bytedance/next-vit"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"the-mean-dimension-of-neural-networks-what","title":"The Mean Dimension of Neural Networks -- What causes the interaction effects?","date":"2022-07-11","arxiv_id":"2207.04890","n_code_links":0,"syntology":null},{"paper":null,"slug":"text-to-image-synthesis-using-stacked","title":"Text to Image Synthesis using Stacked Conditional Variational Autoencoders and Conditional Generative Adversarial Networks","date":"2022-07-06","arxiv_id":"2207.03332","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-mechanisms-for-physiological-signal","title":"Attention mechanisms for physiological signal deep learning: which attention should we take?","date":"2022-07-04","arxiv_id":"2207.06904","n_code_links":0,"syntology":null},{"paper":null,"slug":"game-state-learning-via-game-scene","title":"Game State Learning via Game Scene Augmentation","date":"2022-07-04","arxiv_id":"2207.01289","n_code_links":0,"syntology":null},{"paper":null,"slug":"positive-negative-equal-contrastive-loss-for","title":"Positive-Negative Equal Contrastive Loss for Semantic Segmentation","date":"2022-07-04","arxiv_id":"2207.01417","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-alphazero-inspired-approach-to-solving","title":"An AlphaZero-Inspired Approach to Solving Search Problems","date":"2022-07-02","arxiv_id":"2207.00919","n_code_links":0,"syntology":null},{"paper":"/paper/sequence-aware-multimodal-page-classification","slug":"sequence-aware-multimodal-page-classification","title":"Sequence-aware multimodal page classification of Brazilian legal documents","date":"2022-07-02","arxiv_id":"2207.00748","n_code_links":1,"syntology":null},{"paper":"/paper/dissecting-self-supervised-learning-methods","slug":"dissecting-self-supervised-learning-methods","title":"Dissecting Self-Supervised Learning Methods for Surgical Computer Vision","date":"2022-07-01","arxiv_id":"2207.00449","n_code_links":1,"syntology":null},{"paper":null,"slug":"sparse-periodic-systolic-dataflow-for","title":"Sparse Periodic Systolic Dataflow for Lowering Latency and Power Dissipation of Convolutional Neural Network Accelerators","date":"2022-06-30","arxiv_id":"2207.00068","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-neural-networks-pruning-via-the","title":"Deep Neural Networks pruning via the Structured Perspective Regularization","date":"2022-06-28","arxiv_id":"2206.14056","n_code_links":0,"syntology":null},{"paper":null,"slug":"gan-based-super-resolution-and-segmentation","title":"GAN-based Super-Resolution and Segmentation of Retinal Layers in Optical coherence tomography Scans","date":"2022-06-28","arxiv_id":"2206.13740","n_code_links":0,"syntology":null},{"paper":null,"slug":"generating-near-infrared-facial-expression","title":"Generating near-infrared facial expression datasets with dimensional affect labels","date":"2022-06-28","arxiv_id":"2206.13887","n_code_links":0,"syntology":null},{"paper":"/paper/robustifying-vision-transformer-without","slug":"robustifying-vision-transformer-without","title":"Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature Alignment","date":"2022-06-28","arxiv_id":"2206.13951","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["kojima-takeshi188/cfa"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"stain-isolation-based-guidance-for-improved","title":"Stain Isolation-based Guidance for Improved Stain Translation","date":"2022-06-28","arxiv_id":"2207.00431","n_code_links":0,"syntology":null},{"paper":null,"slug":"studying-generalization-through-data","title":"Studying Generalization Through Data Averaging","date":"2022-06-28","arxiv_id":"2206.13669","n_code_links":0,"syntology":null},{"paper":"/paper/benchopt-reproducible-efficient-and","slug":"benchopt-reproducible-efficient-and","title":"Benchopt: Reproducible, efficient and collaborative optimization benchmarks","date":"2022-06-27","arxiv_id":"2206.13424","n_code_links":3,"syntology":{"ran":6,"of":10,"n_ran_checked":6,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["benchopt/benchopt"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}}],"record_sha256":"7285641378f3c050da2ff12c759bfa7ff85bc33dbc1dd658890cf8d2aea93a8b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}