diff --git a/inference-engine/src/gna_plugin/backend/dnn_types.h b/inference-engine/src/gna_plugin/backend/dnn_types.h
index 223fb224203bcf..38082c2fd5773b 100644
--- a/inference-engine/src/gna_plugin/backend/dnn_types.h
+++ b/inference-engine/src/gna_plugin/backend/dnn_types.h
@@ -71,7 +71,7 @@ struct DnnActivation {
         return type;
     }
     static DnnActivation fromType(DnnActivationType type) {
-        DnnActivation activation;
+        DnnActivation activation{};
         activation.type = type;
         activation.args = {};
         return activation;
diff --git a/inference-engine/src/gna_plugin/layers/gna_fake_quantize_layer.hpp b/inference-engine/src/gna_plugin/layers/gna_fake_quantize_layer.hpp
index a9064292355649..bd0fa55490292c 100644
--- a/inference-engine/src/gna_plugin/layers/gna_fake_quantize_layer.hpp
+++ b/inference-engine/src/gna_plugin/layers/gna_fake_quantize_layer.hpp
@@ -26,7 +26,7 @@ class GNAFakeQuantizeLayer {
      * @brief convert FQ layer directly to gna-pwl activation layer
      */
     DnnActivation parseAsActivation() const {
-        DnnActivation fqActivation;
+        DnnActivation fqActivation{};
 
         fqActivation.fqParams.levels = fqLayer->GetParamAsSizeT("levels");
         auto inputShape  = getShapeForRange(fqLayer, 1);
diff --git a/inference-engine/src/gna_plugin/optimizer/gna_pass_manager.cpp b/inference-engine/src/gna_plugin/optimizer/gna_pass_manager.cpp
index f12135f66b27db..72467598318c3c 100644
--- a/inference-engine/src/gna_plugin/optimizer/gna_pass_manager.cpp
+++ b/inference-engine/src/gna_plugin/optimizer/gna_pass_manager.cpp
@@ -2083,6 +2083,7 @@ void MoveFakeQuantizeLayerIntoQuantParamsPass :: run() {
         };
 
         auto quantParams = InferenceEngine::getInjectedData<QuantizedLayerParams>(layer);
+        IE_ASSERT(quantParams != nullptr);
 
         // Find all output layers connected to FQ
         auto nextLayers = CNNNetGetAllNextLayersSkipCertain(layer.get(), -1, donotSkip);
@@ -2296,7 +2297,7 @@ void TransposeWeightsFromNCHWToNHWCPass::run() {
                 }
             }
             // Find a convolution in next layers to rotate weights columns
-            if (!l->outData.empty() && !getInputTo(l->outData[0]).empty() && !l->outData.empty() && !getInputTo(l->outData[0]).empty()) {
+            if (!l->outData.empty() && !getInputTo(l->outData[0]).empty()) {
                 std::vector<TranspositionInfo> transpositionInfo;
                 auto nextLayer = getInputTo(l->outData[0]).begin()->second;
                 transpositionInfo = FindTranspositionInfoFromNextLayers(nextLayer);
@@ -2337,7 +2338,7 @@ void TransposeWeightsFromNCHWToNHWCPass::run() {
             }
             // Find a convolution in previous or next layers
             auto transpositionInfo = FindTranspositionInfoFromPrevLayers(firstInput);
-            if (!FoundPartToTranspose(transpositionInfo)) {
+            if (!FoundPartToTranspose(transpositionInfo) && !l->outData.empty() && !getInputTo(l->outData[0]).empty()) {
                 transpositionInfo = FindTranspositionInfoFromNextLayers(getInputTo(l->outData[0]).begin()->second);
             }
             if (FoundPartToTranspose(transpositionInfo)) {