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Yapay ZekâÖnerilenAIML-CV-011

Self-Supervised Visual Representations

Learning visual features without labels: contrastive views, masked image modelling, and self-distillation.

Bu konunun henüz Türkçe çevirisi yok, bu yüzden İngilizce gösteriliyor.

Zorluk
4 / 5
Önem
4 / 5
Çalışma süresi
1–2 sa
Çalışma türü
Karma

Neden önemli

The learner can compare contrastive and masked-image pretext tasks and explain how each avoids representation collapse.

Bu konuya giden rota

Rota AIML-CV-011
Doğal Sayılar What Is a Model? Değişkenler ve Sabitler Tasks: Regression, Classification, and Beyond Features and Targets Doğrusal Denklemleri Çözme Fonksiyon Gösterimi ve Bileşke Learning Paradigms: Where the Supervision Signal Comes From Classification Framing Linear Regression I: Prediction and Residuals Embeddings Images as Tensors Kompleks Sayılar Scores and Logits Self-Supervised Learning Loss and Fitting: Squared Error Similarity in Vector Space Temel Trigonometrik Oranlar Multiclass Scores and Softmax The Artificial Neuron Objective Functions and Empirical Risk Polinom Fonksiyonlar Activation Functions Likelihood Convolution Türevin Tanımı Türev Alma Kuralları The Multilayer Perceptron Cross-Entropy Loss Filters and Feature Maps Zincir Kuralı Forward Propagation Contrastive Learning Pooling Computational Graphs CNN Hierarchy The Chain Rule in Neural Nets Backpropagation Gradient Flow and Depth Residual Connections CNN Architectures and Residual Networks Self-Supervised Visual Representations