{"list":"other","total":53,"rows":[{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"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":"2302.13971","title":"LLaMA: Open and Efficient Foundation Language Models","created":"2023-02-27","cited_by_count":14132},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"2304.07193","title":"DINOv2: Learning Robust Visual Features without Supervision","created":"2023-04-14","cited_by_count":7974},{"id":"2001.08361","title":"Scaling Laws for Neural Language Models","created":"2020-01-23","cited_by_count":6586},{"id":"1612.00796","title":"Overcoming catastrophic forgetting in neural networks","created":"2016-12-02","cited_by_count":4787},{"id":"1503.03832","title":"FaceNet: A Unified Embedding for Face Recognition and Clustering","created":"2015-03-12","cited_by_count":2849},{"id":"2203.05482","title":"Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time","created":"2022-03-10","cited_by_count":1396},{"id":"2102.08981","title":"Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts","created":"2021-02-17","cited_by_count":1216},{"id":"1511.07247","title":"NetVLAD: CNN architecture for weakly supervised place recognition","created":"2015-11-23","cited_by_count":941},{"id":"1704.08063","title":"SphereFace: Deep Hypersphere Embedding for Face Recognition","created":"2017-04-26","cited_by_count":737},{"id":"1912.02292","title":"Deep Double Descent: Where Bigger Models and More Data Hurt","created":"2019-12-04","cited_by_count":672},{"id":"2405.07987","title":"The Platonic Representation Hypothesis","created":"2024-05-13","cited_by_count":382},{"id":"1708.02072","title":"Measuring Catastrophic Forgetting in Neural Networks","created":"2017-08-07","cited_by_count":360},{"id":"1612.06321","title":"Large-Scale Image Retrieval with Attentive Deep Local Features","created":"2016-12-19","cited_by_count":238},{"id":"2103.01486","title":"Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition","created":"2021-03-02","cited_by_count":238},{"id":"1511.07543","title":"Convergent Learning: Do different neural networks learn the same representations?","created":"2015-11-24","cited_by_count":220},{"id":"2303.06674","title":"Universal Instance Perception as Object Discovery and Retrieval","created":"2023-03-12","cited_by_count":192},{"id":"2209.15430","title":"Relative representations enable zero-shot latent space communication","created":"2022-09-30","cited_by_count":191},{"id":"2306.00984","title":"StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners","created":"2023-06-01","cited_by_count":177},{"id":"1901.08428","title":"Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group","created":"2019-01-24","cited_by_count":134},{"id":"2210.14891","title":"Broken Neural Scaling Laws","created":"2022-10-26","cited_by_count":109},{"id":"2004.00713","title":"Memory-Efficient Incremental Learning Through Feature Adaptation","created":"2020-04-01","cited_by_count":107},{"id":"1709.06079","title":"Orthogonal Weight Normalization: Solution to Optimization over Multiple Dependent Stiefel Manifolds in Deep Neural Networks","created":"2017-09-16","cited_by_count":92},{"id":"2003.11942","title":"Towards Backward-Compatible Representation Learning","created":"2020-03-26","cited_by_count":75},{"id":"2103.12236","title":"Instance-level Image Retrieval using Reranking Transformers","created":"2021-03-22","cited_by_count":63},{"id":"2011.09161","title":"Positive-Congruent Training: Towards Regression-Free Model Updates","created":"2020-11-18","cited_by_count":57},{"id":"2304.01663","title":"On the Stability-Plasticity Dilemma of Class-Incremental Learning","created":"2023-04-04","cited_by_count":56},{"id":"2311.00664","title":"Latent Space Translation via Semantic Alignment","created":"2023-11-01","cited_by_count":52},{"id":"2408.07057","title":"A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning","created":"2024-08-13","cited_by_count":52},{"id":"2104.13643","title":"On the Unreasonable Effectiveness of Centroids in Image Retrieval","created":"2021-04-28","cited_by_count":45},{"id":"2502.04263","title":"Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion","created":"2025-02-06","cited_by_count":43},{"id":"1809.09924","title":"Hierarchy-based Image Embeddings for Semantic Image Retrieval","created":"2018-09-26","cited_by_count":41},{"id":"2108.01958","title":"Learning Compatible Embeddings","created":"2021-08-04","cited_by_count":37},{"id":"2110.07905","title":"Towards Better Plasticity-Stability Trade-off in Incremental Learning: A Simple Linear Connector","created":"2021-10-15","cited_by_count":34},{"id":"2008.04821","title":"Unified Representation Learning for Cross Model Compatibility","created":"2020-08-11","cited_by_count":30},{"id":"2006.16331","title":"Asymmetric metric learning for knowledge transfer","created":"2020-06-29","cited_by_count":29},{"id":"2203.01583","title":"Towards Universal Backward-Compatible Representation Learning","created":"2022-03-03","cited_by_count":21},{"id":"2112.02805","title":"Forward Compatible Training for Large-Scale Embedding Retrieval Systems","created":"2021-12-06","cited_by_count":19},{"id":"1611.05927","title":"Generalized BackPropagation, \\'{E}tude De Cas: Orthogonality","created":"2016-11-17","cited_by_count":17},{"id":"1610.07008","title":"Optimization on Submanifolds of Convolution Kernels in CNNs","created":"2016-10-22","cited_by_count":15},{"id":"2407.09435","title":"MUSCLE: A Model Update Strategy for Compatible LLM Evolution","created":"2024-07-12","cited_by_count":15},{"id":"2111.07632","title":"CoReS: Compatible Representations via Stationarity","created":"2021-11-15","cited_by_count":14},{"id":"2305.02610","title":"Boundary-aware Backward-Compatible Representation via Adversarial Learning in Image Retrieval","created":"2023-05-04","cited_by_count":11},{"id":"2406.15057","title":"Latent Space Translation via Inverse Relative Projection","created":"2024-06-21","cited_by_count":10},{"id":"2204.13919","title":"Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrieval","created":"2022-04-29","cited_by_count":9},{"id":"2211.03989","title":"$BT^2$: Backward-compatible Training with Basis Transformation","created":"2022-11-08","cited_by_count":8},{"id":"2405.02581","title":"Stationary Representations: Optimally Approximating Compatibility and Implications for Improved Model Replacements","created":"2024-05-04","cited_by_count":8},{"id":"2009.09590","title":"Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation","created":"2020-09-21","cited_by_count":7},{"id":"2303.04766","title":"FastFill: Efficient Compatible Model Update","created":"2023-03-08","cited_by_count":7},{"id":"2211.09032","title":"CL2R: Compatible Lifelong Learning Representations","created":"2022-11-16","cited_by_count":6},{"id":"2408.08793","title":"Backward-Compatible Aligned Representations via an Orthogonal Transformation Layer","created":"2024-08-16","cited_by_count":3}],"next_cursor":null}