Draw a research paper architecture diagram emphasizing the key contribution of the model. Model: Two-Branch CDAN for Gas Sensor Drift Adaptation Key idea to highlight: - Decoupling steady-state (drift-stable) and transient (dynamic) features - Learning complementary representations via dual branches - Aligning domains using conditional adversarial learning (CDAN) Requirements: - Clearly separate two branches: 1) Steady Branch (low-frequency, drift-robust features) 2) Transient Branch (high-frequency dynamic response features) - Add a label above branches: "Steady–Transient Decoupled Representation Learning" - Show feature fusion after the two branches - Show classifier shared between domains - Show CDAN module: - Feature + prediction interaction (outer product) - Projection - GRL - Domain discriminator - Use one unified pipeline (no duplicated source/target networks) - Represent: - Source flow with solid arrows - Target flow with dashed arrows - Use clear grouping boxes: - Dual-Branch Feature Extractor - CDAN Module - Style: - Minimalist, academic, vector-style - No unnecessary detail - Balanced layout (left-to-right) Goal: Make the diagram clearly communicate WHY two branches are needed and HOW CDAN performs alignment.

Draw a clean academic-style architecture diagram for a deep learning model, suitable for a research paper (CVPR/NeurIPS style). Model: Two-Branch CDAN for Gas Sensor Drift Adaptation Structure: 1. Input: - Gas sensor data with shape (batch, 16 sensors, 8 features) 2. Two-Branch Feature Extractor: (a) Steady Branch: - Input: first 2 features per sensor (steady-state features) - Flattened - Passed through: - Linear projection - Two Gated Residual MLP Blocks (LayerNorm + gating mechanism) - Output: 64-dim feature (b) Transient Branch: - Input: last 6 features per sensor (transient features) - Passed through: - Linear embedding - Sensor embedding (positional encoding) - Multi-head self-attention - Feed-forward network - Attention pooling - Output: 128-dim feature 3. Feature Fusion: - Concatenate two branches - Linear + LayerNorm + GELU - Output: 200-dim feature 4. Label Classifier: - Fully connected layer for classification 5. CDAN Module: - Outer product between features and softmax predictions - Projection layer (dim reduction) - Domain discriminator (MLP) - Gradient Reversal Layer (GRL) 6. Training: - Classification loss (source domain) - Adversarial domain loss (source + target) Requirements: - Use clear block diagram style - Show two parallel branches explicitly - Use arrows for data flow - Distinguish source and target streams (e.g., colors or dashed lines) - Label key components (Steady Branch, Transient Branch, Fusion, CDAN) - Clean, minimal, publication-quality design

я прикрепил фотографию это Кухня-гостинная - она максимально отображает как я хочу расставить мебель. но оттенки - цвета мебели и интерьера мне не нравятся, прикрепил ещё несколько скриншотов с интерьером и дизайном который мне по душе. нарисуй мне 4 вариации моей комнаты в этом стиле.. так же хочу добавить что мне нравится когда есть небольшие элементы с деревом, натуральным камнем и немного зелени или цветов. Учти что вся мебель должна остаться как есть, мне хочется только что бы были различные вариации дизайна - интерьера.

Remove all text from this image, clean background, high quality.

Remove all text from this image, clean background, high quality.

aggiungi un aquila simbolo, le ali con piume realistiche . illumina corpo

Ultra detailed realistic collectible statue scene inspired by Wacky Races. Three iconic racers competing in a dramatic moment on a dusty race track: • Dick Dastardly standing inside the Mean Machine vehicle while plotting a trick • Penelope Pitstop driving her elegant pink Compact Pussycat • Professor Pat Pending controlling the gadget-filled Convert-A-Car The vehicles are positioned dynamically as if racing side by side through a dusty road curve. Highly detailed vehicles faithful to the original cartoon designs: • Mean Machine with dark purple body and mechanical trap devices • Compact Pussycat bright pink elegant racing car • Convert-A-Car filled with mechanical gadgets, propellers and inventions Dust clouds and motion effects rise from the wheels while the racers compete intensely. Environment designed as a cinematic desert race track with wooden signs, rocks and scattered mechanical debris. Realistic materials and lighting: metal reflections, dust particles in the air, dramatic sunlight and shadows. Photorealistic collectible statue scene, ultra detailed sculpt, cinematic lighting, professional studio render of a premium display statue.

Ultra detailed realistic collectible statue scene inspired by Wacky Races. Three iconic racers competing in a dramatic moment on a dusty race track: • Dick Dastardly standing inside the Mean Machine vehicle while plotting a trick • Penelope Pitstop driving her elegant pink Compact Pussycat • Professor Pat Pending controlling the gadget-filled Convert-A-Car The vehicles are positioned dynamically as if racing side by side through a dusty road curve. Highly detailed vehicles faithful to the original cartoon designs: • Mean Machine with dark purple body and mechanical trap devices • Compact Pussycat bright pink elegant racing car • Convert-A-Car filled with mechanical gadgets, propellers and inventions Dust clouds and motion effects rise from the wheels while the racers compete intensely. Environment designed as a cinematic desert race track with wooden signs, rocks and scattered mechanical debris. Realistic materials and lighting: metal reflections, dust particles in the air, dramatic sunlight and shadows. Photorealistic collectible statue scene, ultra detailed sculpt, cinematic lighting, professional studio render of a premium display statue.