{"id":62168,"date":"2025-04-08T15:48:19","date_gmt":"2025-04-08T15:48:19","guid":{"rendered":""},"modified":"2025-04-08T15:48:19","modified_gmt":"2025-04-08T15:48:19","slug":"%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-62168","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-62168\/","title":{"rendered":"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u0645\u0648\u0631\u062f \u0628\u0647\u0628\u0648\u062f \u062a\u0639\u0645\u06cc\u0645 \u0648 \u062b\u0628\u0627\u062a \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0641\u0642\u0637 \u0628\u0647 \u062c\u0644\u0648 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0642\u0637\u0628\u0634 \u0639\u0635\u0628\u06cc"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nOn the Improvement of Generalization and Stability of Forward-Only Learning via Neural Polarization<\/td>\n<\/tr>\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u0645\u0648\u0631\u062f \u0628\u0647\u0628\u0648\u062f \u062a\u0639\u0645\u06cc\u0645 \u0648 \u062b\u0628\u0627\u062a \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0641\u0642\u0637 \u0628\u0647 \u062c\u0644\u0648 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0642\u0637\u0628\u0634 \u0639\u0635\u0628\u06cc<\/td>\n<\/tr>\n
\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\nErik B. Terres-Escudero, Javier Del Ser, Pablo Garcia-Bringas<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nPDF<\/td>\n<\/tr>\n
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\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n15<\/td>\n<\/tr>\n
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\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a <\/td>\nMachine Learning,Artificial Intelligence,Neural and Evolutionary Computing,\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 , \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc , \u0645\u062d\u0627\u0633\u0628\u0627\u062a \u0639\u0635\u0628\u06cc \u0648 \u062a\u06a9\u0627\u0645\u0644\u06cc ,<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a <\/td>\nSubmitted 11 September, 2024; v1 submitted 17 August, 2024; originally announced August 2024. , Comments: Accepted in European Conference on Artificial Intelligence (ECAI), 2024<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 \u062f\u0631 11 \u0633\u067e\u062a\u0627\u0645\u0628\u0631 2024 \u061bV1 \u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 17 \u0627\u0648\u062a 2024 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0627\u0648\u062a 2024 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f \u060c \u0646\u0638\u0631\u0627\u062a: \u062f\u0631 \u06a9\u0646\u0641\u0631\u0627\u0646\u0633 \u0627\u0631\u0648\u067e\u0627 \u062f\u0631 \u0632\u0645\u06cc\u0646\u0647 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc (ECAI) \u060c 2024 \u067e\u0630\u06cc\u0631\u0641\u062a\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a<\/td>\n<\/tr>\n
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\n
\n NASA ADS<\/a>
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\n Google Scholar<\/a>
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\n Semantic Scholar<\/a>
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\n arXiv<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n \r\n \r\n \r\n \r\n
\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n <\/tr>\r\n
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\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 <\/td>\r\n \u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n <\/tr>\r\n<\/table>\r\n\r\n\n

\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n

Forward-only learning algorithms have recently gained attention as alternatives to gradient backpropagation, replacing the backward step of this latter solver with an additional contrastive forward pass. Among these approaches, the so-called Forward-Forward Algorithm (FFA) has been shown to achieve competitive levels of performance in terms of generalization and complexity. Networks trained using FFA learn to contrastively maximize a layer-wise defined goodness score when presented with real data (denoted as positive samples) and to minimize it when processing synthetic data (corr. negative samples). However, this algorithm still faces weaknesses that negatively affect the model accuracy and training stability, primarily due to a gradient imbalance between positive and negative samples. To overcome this issue, in this work we propose a novel implementation of the FFA algorithm, denoted as Polar-FFA, which extends the original formulation by introducing a neural division (\\emph{polarization}) between positive and negative instances. Neurons in each of these groups aim to maximize their goodness when presented with their respective data type, thereby creating a symmetric gradient behavior. To empirically gauge the improved learning capabilities of our proposed Polar-FFA, we perform several systematic experiments using different activation and goodness functions over image classification datasets. Our results demonstrate that Polar-FFA outperforms FFA in terms of accuracy and convergence speed. Furthermore, its lower reliance on hyperparameters reduces the need for hyperparameter tuning to guarantee optimal generalization capabilities, thereby allowing for a broader range of neural network configurations.<\/p>\n

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\u0637\u0631\u06cc\u0642 \u06cc\u06a9 \u0631\u0641\u062a\u0627\u0631 \u0634\u06cc\u0628 \u0645\u062a\u0642\u0627\u0631\u0646 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u0646\u062f.\u0628\u0631\u0627\u06cc \u0633\u0646\u062c\u0634 \u062a\u062c\u0631\u0628\u06cc \u0642\u0627\u0628\u0644\u06cc\u062a \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0628\u0647\u0628\u0648\u062f \u06cc\u0627\u0641\u062a\u0647 \u0627\u0632 \u0642\u0637\u0628\u06cc Polar-FFA \u060c \u0645\u0627 \u0686\u0646\u062f\u06cc\u0646 \u0622\u0632\u0645\u0627\u06cc\u0634 \u0633\u06cc\u0633\u062a\u0645\u0627\u062a\u06cc\u06a9 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0648\u0627\u0628\u0639 \u0641\u0639\u0627\u0644 \u0633\u0627\u0632\u06cc \u0648 \u062e\u0648\u0628 \u0628\u0648\u062f\u0646 \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u062a\u0635\u0648\u06cc\u0631 \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc \u062f\u0647\u06cc\u0645.\u0646\u062a\u0627\u06cc\u062c \u0645\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u06a9\u0647 \u0642\u0637\u0628\u06cc-FFA \u0627\u0632 \u0646\u0638\u0631 \u062f\u0642\u062a \u0648 \u0633\u0631\u0639\u062a \u0647\u0645\u06af\u0631\u0627\u06cc\u06cc \u0627\u0632 FFA \u0628\u0647\u062a\u0631 \u0627\u0633\u062a.\u0639\u0644\u0627\u0648\u0647 \u0628\u0631 \u0627\u06cc\u0646 \u060c \u0627\u0639\u062a\u0645\u0627\u062f \u0628\u0647 \u0646\u0641\u0633 \u067e\u0627\u06cc\u06cc\u0646 \u062a\u0631 \u0622\u0646 \u0628\u0647 HyperParameters \u0646\u06cc\u0627\u0632 \u0628\u0647 \u062a\u0646\u0638\u06cc\u0645 Hyperparameter \u0631\u0627 \u0628\u0631\u0627\u06cc \u062a\u0636\u0645\u06cc\u0646 \u0642\u0627\u0628\u0644\u06cc\u062a \u0647\u0627\u06cc \u062a\u0639\u0645\u06cc\u0645 \u0628\u0647\u06cc\u0646\u0647 \u06a9\u0627\u0647\u0634 \u0645\u06cc \u062f\u0647\u062f \u060c \u062f\u0631 \u0646\u062a\u06cc\u062c\u0647 \u0627\u0645\u06a9\u0627\u0646 \u0637\u06cc\u0641 \u06af\u0633\u062a\u0631\u062f\u0647 \u0627\u06cc \u0627\u0632 \u062a\u0646\u0638\u06cc\u0645\u0627\u062a \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0631\u0627 \u0641\u0631\u0627\u0647\u0645 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n\r\n\r\n \r\n \r\n \r\n \r\n
\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n <\/tr>\r\n
\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n <\/tr>\r\n\t
\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 <\/td>\r\n \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n <\/tr>\r\n\t\t
\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 <\/td>\r\n \u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n <\/tr>\r\n<\/table>\r\n\r\n\n","protected":false},"excerpt":{"rendered":"

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