Update README.md

Author: ashishpatel26

README.md

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 # GRADCAM-Tensorflow2-Visual-Explainable-AI
-Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
+###  Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
+
+* Install Grad CAM : `!pip install tf-explain`
+* src : https://github.com/sicara/tf-explain
+* paper : Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
+* Reference : https://arxiv.org/abs/1610.02391
+* Abstract : We propose a technique for producing "visual explanations" for decisions from a large class
+  of CNN-based models, making them more transparent. Our approach - Gradient-weighted Class Activation Mapping 
+  (Grad-CAM), uses the gradients of any target concept, flowing into the final convolutional layer to produce 
+  a coarse localization map highlighting important regions in the image for predicting the concept. Grad-CAM 
+  is applicable to a wide variety of CNN model-families: 
+  (1) CNNs with fully-connected layers, 
+  (2) CNNs used for structured outputs, 
+  (3) CNNs used in tasks with multimodal inputs or reinforcement learning, 
+  without any architectural changes or re-training. We combine Grad-CAM with fine-grained visualizations to create
+   a high-resolution class-discriminative visualization and apply it to off-the-shelf image classification, captioning, 
+   and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification 
+   models, our visualizations (a) lend insights into their failure modes, 
+   (b) are robust to adversarial images, (c) outperform previous methods on localization, (d) are more faithful to the 
+   underlying model and (e) help achieve generalization by identifying dataset bias. For captioning and VQA, we show that even
+    non-attention based models can localize inputs. We devise a way to identify important neurons through Grad-CAM and combine it 
+    with neuron names to provide textual explanations for model decisions. Finally, we design and conduct human studies to measure 
+    if Grad-CAM helps users establish appropriate trust in predictions from models and show that Grad-CAM helps untrained users 
+    successfully discern a 'stronger' nodel from a 'weaker' one even when both make identical predictions. 
+
+* Note : you can pass `model` object as any tensorflow keras model.